ACL Wiki - User contributions [en]

Graph Parsing (State of the Art)

2016-06-07T20:45:51Z

Oe: /* SDP: Semantic Dependency Parsing */

= Background and Motivation =

Graphs exceeding the formal complexity of rooted trees are of
growing relevance to much NLP research.
We interpret the term ''graph parsing'' broadly as mapping from surface
strings to graph-structured target representations, which typically
provide some level of syntactico-semantic analysis.
Although formally well-understood in graph theory, there is
substantial variation in the types of linguistic graphs, as well as
in the interpretation of various structural properties.
To provide a common terminology and transparent statistics across
different collections of graphs in NLP, we propose to establish
a ‘catalogue’ of graph banks and associated parsing results.

We anticipate a bit of a cottage industry in linguistic graph banks and
graph processing tasks over the next few years, which may make
it difficult to keep track of contentful similarities
and differences across frameworks and approaches.
This page is intended to stimulate community work towards an up-to-date
resource combining the following components: (a) formal definitions of
(relevant) structural graph properties; (b) in-depth descriptions of how
these apply to different graph banks; (c) constantly growing surveys of
graph bank statistics; and (d) a continuously evolving record of
state-of-the-art processing results.
Of these, components (a) and (b) are provided by
Kuhlmann and Oepen (2016)<ref name="kuhlmann2016">Marco Kuhlmann and Stephan Oepen. Towards a Catalogue of Linguistic Graph Banks. Computational Linguistics, 2016. In press. [http://www.mn.uio.no/ifi/english/people/aca/oe/cl.pdf Preprint]</ref>,
while (c) and (d) are maintained below.

This page was initiated by
[http://www.ida.liu.se/~marku61/ Marco Kuhlmann] and
[http://www.mn.uio.no/ifi/english/people/aca/oe/ Stephan Oepen], and for the
time being (mid-May 2016) is very much a work in progress.
We intend to have a first complete draft available for community review
by early June 2016.

= Software: Graph Analysis Toolkit =

An open-source reference implementation of the toolkit that was built to conduct the study of Kuhlmann and Oepen (2016)<ref name="kuhlmann2016"/> will be available later this summer.

= AMR: Abstract Meaning Representation =

Abstract Meaning Representation (AMR) eschews explicit syntactic derivations and consideration of the syntax–semantics interface; it rather seeks to directly annotate “whole-sentence logical meanings” (Banarescu et al. 2013<ref name="banarescu2013">Banarescu, Laura, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. Abstract Meaning Representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 178–186, Sofia, Bulgaria, August.</ref>). Node labels in AMR name abstract concepts, which in large parts draw on the ontology of OntoNotes predicate senses and corresponding semantic roles. Nodes are not overtly related to surface lexical units, and thus are unordered. Although AMR has its roots in semantic networks and earlier knowledge representation approaches (Langkilde and Knight 1998<ref name="langkilde1998">Langkilde, Irene and Kevin Knight. 1998. Generation that exploits corpus-based statistical
knowledge. In Proceedings of the 17th International Conference on Computational Linguistics and the 36th Meeting of the Association for Computational Linguistics, pages 704–710, Montréal, Canada.</ref>), larger-scale manual AMR annotation is a recent development only. We sample two variants of AMR, viz. (a) the graphs as annotated in AMRBank 1.0 (LDC2014T12), and (b) a normalized version that we call AMR−1, where so-called “inverse roles” (like ARG0-of) are reversed. Such inverted edges are frequently used in AMR in order to render the graph as a single rooted structure, where the root is interpreted as the top-level focus. (The graph bank is natively constructed and released with inverted edges, but for parser evaluation the AMR−1 normalization is typically assumed; our conversion builds on the code of Cai and Knight (2013)<ref name="cai2013">Cai, Shu and Kevin Knight. 2013. Smatch. An evaluation metric for semantic feature structures. In Proceedings of the 51th Meeting of the Association for Computational Linguistics, pages 748–752, Sofia, Bulgaria, August.</ref>.) In the context of this comparison, we map this interpretation to a general concept of “top nodes” for both AMR and AMR−1. Flanigan et al. (2014)<ref name="flanigan2014">Flanigan, Jeffrey, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A. Smith. 2014. A discriminative graph-based parser for the Abstract Meaning Representation. In Proceedings of the 52nd Meeting of the Association for Computational Linguistics, pages 1426–1436, Baltimore, MD, USA, June.</ref> published the first parser targeting AMR, and the state of the art has been repeatedly updated since.

{|
! style="text-align:left;"| Property
! style="text-align:center;"| AMR
! style="text-align:center;"| AMR−1
|-
|number of graphs
|style="text-align:right;"|10309
|style="text-align:right;"|10309
|-
|average number of tokens
|style="text-align:right;"|20.62
|style="text-align:right;"|20.62
|-
|average number of nodes per token
|style="text-align:right;"|0.67
|style="text-align:right;"|0.67
|-
|number of edge labels
|style="text-align:right;"|135
|style="text-align:right;"|100
|-
|percentage of graphs that are trees
|style="text-align:right;"|52.48
|style="text-align:right;"|18.60
|-
|percentage of graphs with treewidth one
|style="text-align:right;"|52.72
|style="text-align:right;"|52.72
|-
|average treewidth
|style="text-align:right;"|1.524
|style="text-align:right;"|1.524
|-
|maximal treewidth
|style="text-align:right;"|4
|style="text-align:right;"|4
|-
|average edge density
|style="text-align:right;"|1.065
|style="text-align:right;"|1.065
|-
|percentage of nodes that are reentrant
|style="text-align:right;"|5.23
|style="text-align:right;"|18.95
|-
|percentage of graphs that are cyclic
|style="text-align:right;"|3.15
|style="text-align:right;"|0.71
|-
|percentage of graphs that are not connected
|style="text-align:right;"|0.00
|style="text-align:right;"|0.00
|-
|percentage of graphs that are multi-rooted
|style="text-align:right;"|0.00
|style="text-align:right;"|77.50
|-
|percentage of non-top roots
|style="text-align:right;"|47.78
|style="text-align:right;"|19.39
|}

= CCD: Combinatory Categorial Grammar Dependencies =

Hockenmaier and Steedman (2007)<ref name="hockenmaier2007">Hockenmaier, Julia and Mark Steedman. 2007. CCGbank. A corpus of CCG derivations and dependency structures extracted from the Penn Treebank. Computational Linguistics, 33:355–396.</ref> construct CCGbank from a combination of careful interpretation of the syntactic annotations in the PTB with additional, manually curated lexical and constructional knowledge. In CCGbank (LDC2005T13), the strings of the venerable PTB Wall Street Journal (WSJ) corpus are annotated with pairs of (a) CCG syntactic derivations and (b) sets of semantic bi-lexical dependency triples, which we term CCD. The latter “include most semantically relevant non-anaphoric local and long-range dependencies” and are suggested by the CCGbank creators as a proxy for predicate–argument structure. While CCD has mainly been used for contrastive parser evaluation (Clark and Curran [2007]<ref name="clark2007">Clark, Stephen and James R. Curran. 2007. Wide-coverage efficient statistical parsing with CCG and log-linear models. Computational Linguistics, 33(4):493–552.</ref>, Fowler and Penn [2010]<ref name="fowler2010">Fowler, Timothy A. D. and Gerald Penn. 2010. Accurate context-free parsing with Combinatory Categorial Grammar. In Proceedings of the 48th Meeting of the Association for Computational Linguistics, pages 335–344, Uppsala, Sweden.</ref>; inter alios), there is current work that views each set of triples as a directed graph and parses directly into these target representations (Du, Sun, and Wan 2015<ref name="du2015">Du, Yantao, Weiwei Sun, and Xiaojun Wan. 2015. A data-driven, factorization parser for CCG dependency structures. In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and of the 7th International Joint Conference on Natural Language Processing, pages 1545–1555, Bejing, China.</ref>).

{|
! style="text-align:left;"| Property
! style="text-align:center;"| CCD
|-
|number of graphs
|style="text-align:right;"|39604
|-
|average number of tokens
|style="text-align:right;"|23.47
|-
|average number of nodes per token
|style="text-align:right;"|0.88
|-
|number of edge labels
|style="text-align:right;"|6
|-
|percentage of graphs that are trees
|style="text-align:right;"|1.45
|-
|percentage of graphs with treewidth one
|style="text-align:right;"|29.27
|-
|average treewidth
|style="text-align:right;"|1.742
|-
|maximal treewidth
|style="text-align:right;"|5
|-
|average edge density
|style="text-align:right;"|1.070
|-
|percentage of nodes that are reentrant
|style="text-align:right;"|28.09
|-
|percentage of graphs that are cyclic
|style="text-align:right;"|1.28
|-
|percentage of graphs that are not connected
|style="text-align:right;"|12.53
|-
|percentage of graphs that are multi-rooted
|style="text-align:right;"|99.67
|-
|percentage of non-top roots
|style="text-align:right;"|47.78
|-
|average edge length
|style="text-align:right;"|2.582
|-
|percentage of graphs that are noncrossing
|style="text-align:right;"|48.23
|-
|percentage of graphs with pagenumber at most two
|style="text-align:right;"|98.64
|}

= EDS: Elementary Dependency Structures =

= SDP: Semantic Dependency Parsing =

= UCCA: Universal Conceptual Cognitive Annotation =

= References =

Graph Parsing (State of the Art)

2016-05-30T09:26:18Z

Oe: /* SDP: Semantic Dependency Parsing */

= Background and Motivation =

Graphs exceeding the formal complexity of rooted trees are of
growing relevance to much NLP research.
We interpret the term ''graph parsing'' broadly as mapping from surface
strings to graph-structured target representations, which typically
provide some level of syntactico-semantic analysis.
Although formally well-understood in graph theory, there is
substantial variation in the types of linguistic graphs, as well as
in the interpretation of various structural properties.
To provide a common terminology and transparent statistics across
different collections of graphs in NLP, we propose to establish
a ‘catalogue’ of graph banks and associated parsing results.

We anticipate a bit of a cottage industry in linguistic graph banks and
graph processing tasks over the next few years, which may make
it difficult to keep track of contentful similarities
and differences across frameworks and approaches.
This page is intended to stimulate community work towards an up-to-date
resource combining the following components: (a) formal definitions of
(relevant) structural graph properties; (b) in-depth descriptions of how
these apply to different graph banks; (c) constantly growing surveys of
graph bank statistics; and (d) a continuously evolving record of
state-of-the-art processing results.
Of these, components (a) and (b) are provided by
Kuhlmann and Oepen (2016)<ref name="kuhlmann2016">Marco Kuhlmann and Stephan Oepen. Towards a Catalogue of Linguistic Graph Banks. Computational Linguistics, 2016. In press. [http://www.mn.uio.no/ifi/english/people/aca/oe/cl.pdf Preprint]</ref>,
while (c) and (d) are maintained below.

This page was initiated by
[http://www.ida.liu.se/~marku61/ Marco Kuhlmann] and
[http://www.mn.uio.no/ifi/english/people/aca/oe/ Stephan Oepen], and for the
time being (mid-May 2016) is very much a work in progress.
We intend to have a first complete draft available for community review
by early June 2016.

= Software: Graph Analysis Toolkit =

An open-source reference implementation of the toolkit that was built to conduct the study of Kuhlmann and Oepen (2016)<ref name="kuhlmann2016"/> will be available later this summer.

= AMR: Abstract Meaning Representation =

Abstract Meaning Representation (AMR) eschews explicit syntactic derivations and consideration of the syntax–semantics interface; it rather seeks to directly annotate “whole-sentence logical meanings” (Banarescu et al. 2013<ref name="banarescu2013">Banarescu, Laura, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. Abstract Meaning Representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, page 178–186, Sofia, Bulgaria, August.</ref>). Node labels in AMR name abstract concepts, which in large parts draw on the ontology of OntoNotes predicate senses and corresponding semantic roles. Nodes are not overtly related to surface lexical units, and thus are unordered. Although AMR has its roots in semantic networks and earlier knowledge representation approaches (Langkilde and Knight 1998<ref name="langkilde1998">Langkilde, Irene and Kevin Knight. 1998. Generation that exploits corpus-based statistical
knowledge. In Proceedings of the 17th International Conference on Computational Linguistics and the 36th Meeting of the Association for Computational Linguistics, page 704–710, Montréal, Canada.</ref>), larger-scale manual AMR annotation is a recent development only. We sample two variants of AMR, viz. (a) the graphs as annotated in AMRBank 1.0 (LDC2014T12), and (b) a normalized version that we call AMR−1, where so-called “inverse roles” (like ARG0-of) are reversed. Such inverted edges are frequently used in AMR in order to render the graph as a single rooted structure, where the root is interpreted as the top-level focus. (The graph bank is natively constructed and released with inverted edges, but for parser evaluation the AMR−1 normalization is typically assumed; our conversion builds on the code of Cai and Knight (2013)<ref name="cai2013">Cai, Shu and Kevin Knight. 2013. Smatch. An evaluation metric for semantic feature structures. In Proceedings of the 51th Meeting of the Association for Computational Linguistics, page 748–752, Sofia, Bulgaria, August.</ref>.) In the context of this comparison, we map this interpretation to a general concept of “top nodes” for both AMR and AMR−1. Flanigan et al. (2014)<ref name="flanigan2014">Flanigan, Jeffrey, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A. Smith. 2014. A discriminative graph-based parser for the Abstract Meaning Representation. In Proceedings of the 52nd Meeting of the Association for Computational Linguistics, page 1426–1436, Baltimore, MD, USA, June.</ref> published the first parser targeting AMR, and the state of the art has been repeatedly updated since.

{|
! style="text-align:left;"| Property
! style="text-align:center;"| AMR
! style="text-align:center;"| AMR−1
|-
|number of graphs
|style="text-align:right;"|10309
|style="text-align:right;"|10309
|-
|average number of tokens
|style="text-align:right;"|20.62
|style="text-align:right;"|20.62
|-
|average number of nodes per token
|style="text-align:right;"|0.67
|style="text-align:right;"|0.67
|-
|number of edge labels
|style="text-align:right;"|135
|style="text-align:right;"|100
|-
|percentage of graphs that are trees
|style="text-align:right;"|52.48
|style="text-align:right;"|18.60
|-
|percentage of graphs with treewidth one
|style="text-align:right;"|52.72
|style="text-align:right;"|52.72
|-
|average treewidth
|style="text-align:right;"|1.524
|style="text-align:right;"|1.524
|-
|maximal treewidth
|style="text-align:right;"|4
|style="text-align:right;"|4
|-
|average edge density
|style="text-align:right;"|1.065
|style="text-align:right;"|1.065
|-
|percentage of nodes that are reentrant
|style="text-align:right;"|5.23
|style="text-align:right;"|18.95
|-
|percentage of graphs that are cyclic
|style="text-align:right;"|3.15
|style="text-align:right;"|0.71
|-
|percentage of graphs that are not connected
|style="text-align:right;"|0.00
|style="text-align:right;"|0.00
|-
|percentage of graphs that are multi-rooted
|style="text-align:right;"|0.00
|style="text-align:right;"|77.50
|-
|percentage of non-top roots
|style="text-align:right;"|47.78
|style="text-align:right;"|19.39
|}

= CCD: Combinatory Categorial Grammar Dependencies =

Hockenmaier and Steedman (2007)<ref name="hockenmaier2007">Hockenmaier, Julia and Mark Steedman. 2007. CCGbank. A corpus of CCG derivations and dependency structures extracted from the Penn Treebank. Computational Linguistics, 33:355–396.</ref> construct CCGbank from a combination of careful interpretation of the syntactic annotations in the PTB with additional, manually curated lexical and constructional knowledge. In CCGbank (LDC2005T13), the strings of the venerable PTB Wall Street Journal (WSJ) corpus are annotated with pairs of (a) CCG syntactic derivations and (b) sets of semantic bi-lexical dependency triples, which we term CCD. The latter “include most semantically relevant non-anaphoric local and long-range dependencies” and are suggested by the CCGbank creators as a proxy for predicate–argument structure. While CCD has mainly been used for contrastive parser evaluation (Clark and Curran [2007]<ref name="clark2007">Clark, Stephen and James R. Curran. 2007. Wide-coverage efficient statistical parsing with CCG and log-linear models. Computational Linguistics, 33(4):493–552.</ref>, Fowler and Penn [2010]<ref name="fowler2010">Fowler, Timothy A. D. and Gerald Penn. 2010. Accurate context-free parsing with Combinatory Categorial Grammar. In Proceedings of the 48th Meeting of the Association for Computational Linguistics, page 335–344, Uppsala, Sweden.</ref>; inter alios), there is current work that views each set of triples as a directed graph and parses directly into these target representations (Du, Sun, and Wan 2015<ref name="du2015">Du, Yantao, Weiwei Sun, and Xiaojun Wan. 2015. A data-driven, factorization parser for CCG dependency structures. In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and of the 7th International Joint Conference on Natural Language Processing, page 1545–1555, Bejing, China.</ref>).

{|
! style="text-align:left;"| Property
! style="text-align:center;"| CCD
|-
|number of graphs
|style="text-align:right;"|39604
|-
|average number of tokens
|style="text-align:right;"|23.47
|-
|average number of nodes per token
|style="text-align:right;"|0.88
|-
|number of edge labels
|style="text-align:right;"|6
|-
|percentage of graphs that are trees
|style="text-align:right;"|1.45
|-
|percentage of graphs with treewidth one
|style="text-align:right;"|29.27
|-
|average treewidth
|style="text-align:right;"|1.742
|-
|maximal treewidth
|style="text-align:right;"|5
|-
|average edge density
|style="text-align:right;"|1.070
|-
|percentage of nodes that are reentrant
|style="text-align:right;"|28.09
|-
|percentage of graphs that are cyclic
|style="text-align:right;"|1.28
|-
|percentage of graphs that are not connected
|style="text-align:right;"|12.53
|-
|percentage of graphs that are multi-rooted
|style="text-align:right;"|99.67
|-
|percentage of non-top roots
|style="text-align:right;"|47.78
|-
|average edge length
|style="text-align:right;"|2.582
|-
|percentage of graphs that are noncrossing
|style="text-align:right;"|48.23
|-
|percentage of graphs with pagenumber at most two
|style="text-align:right;"|98.64
|}

= EDS: Elementary Dependency Structures =

= SDP: Semantic Dependency Parsing =

= References =

Graph Parsing (State of the Art)

2016-05-23T22:35:42Z

Oe: /* Background and Motivation */

Graph Parsing (State of the Art)

2016-05-09T18:57:33Z

Oe:

Graph Parsing (State of the Art)

2016-05-09T15:37:15Z

Oe: /* Background and Motivation */

Graph Parsing (State of the art)

2016-05-09T11:40:42Z

Oe: Oe moved page Graph Parsing (State of the art) to Graph Parsing (State of the Art): consistency

#REDIRECT [[Graph Parsing (State of the Art)]]

Graph Parsing (State of the Art)

2016-05-09T11:40:42Z

Oe: Oe moved page Graph Parsing (State of the art) to Graph Parsing (State of the Art): consistency

Graph Parsing (State of the Art)

2016-05-09T11:38:38Z

Oe: initial page creation (so there is a URL)

Academic departments

2012-04-03T10:20:42Z

Oe: /* Norway */

==Africa==

===South Africa===
*[http://www.ctext.co.za North-West University, Potchefstroom Campus: Centre for Text Technology (CTexT)]
*[http://www.nu.ac.za/department/default.asp?dept=linguistund University of Natal - Dept. of Linguistics]

==Asia==
*[http://www.ims.uni-stuttgart.de/info/SitesAsia.html Computational Linguistics in Asia]
*[http://www.first-institute.com/ The First Institute Of Dynamic Learning]

===Bangladesh===
*[http://www.bracuniversity.ac.bd/research/crblp BRAC University, Dhaka - Center for Research on Bangla Language Processing]

===China===
*[http://nlp.suda.edu.cn/ Soochow University - Natural Language Processing Lab]
*[http://www.cs.hit.edu.cn/ Harbin Institute of Technology - CS School]
*[http://www.icl.pku.edu.cn/ Peking University - Institute of Computational Linguistics]
*[http://nlp.csai.tsinghua.edu.cn/ Tsinghua University - Group of Natural Language Processing]

===Hong Kong===
*[http://corpora.ctl.cityu.edu.hk/ City University of Hong Kong - The Dialogue Systems Group]
*[http://www.comp.polyu.edu.hk/ Hong Kong Polytechnic University - Department of Computing]
*[http://www.cs.ust.hk/ Hong Kong University of Science and Technology - CS Department]

===India===
*[http://www.iiit.net/ltrc/pgprogrammes.html IIIT, Hyderabad - Academic Programmes]
*[http://www.ciil.org/ Central Institute of Indian Languages]
*[http://www.cdacindia.com/ Centre for Development of Advanced Computing]
*[http://www.hpl.hp.com/india/ HP Research Labs, India]
*[http://www.iisc.ernet.in/ IISc, Bangalore]
*[http://www.isical.ac.in/ Indian Statistical Institute]
*[http://www.iiit.net International Institute of Information Technology, Hyderabad]
*[http://www.au-kbc.org/research_areas/nlp.html Language Technology Research at AU-KBC, Chennai]
*[http://www.cse.iitk.ac.in/users/langtech/hist.htm IIT, Kanpur - Language Technology Research]
*[http://tdil.mit.gov.in/introindx.html TDIL]
*[http://www.uohyd.ernet.in/ University of Hyderabad]

===Israel===
*[http://cl.haifa.ac.il/ University of Haifa - Computational Linguistics Group]
*[http://www.cs.technion.ac.il/~lcl Laboratory for Computational Linguistics, Technion, Israel]
*[http://www.cs.huji.ac.il/~arir/ Hebrew University - NLP at the CS Institute]
*[http://www.cs.biu.ac.il/~nlp NLP lab at Bar-Ilan University]

===Japan===
*[http://cl.naist.jp/en/ Nara Institute of Science and Technology - Computational Linguistics Laboratory]
*[http://www-tsujii.is.s.u-tokyo.ac.jp/ The University of Tokyo - Tsujii group]
*[http://www.r.dl.itc.u-tokyo.ac.jp/index-e.html The University of Tokyo - Language Informatics Laboratory]
*[http://www.lr.pi.titech.ac.jp/en/index.html Tokyo Institute of Technology - Okumura group]

===Korea===
*[http://nlp.korea.ac.kr/ Korea University - NLP lab]
*[http://kle.postech.ac.kr/ The Knowledge and Language Engineering Laboratory]

===Singapore===
*[http://www.comp.nus.edu.sg/~nght/cllab.html National University of Singapore - Computational Linguistics Lab]
*[http://wing.comp.nus.edu.sg/ National University of Singapore - Web, IR / NLP Group (WING)]
*[http://www.ntu.edu.sg/HSS/Linguistics/ Nanyang Technological University - Division of Linguistics and Multilingual Studies]

===Sri Lanka===
*[http://www.ucsc.cmb.ac.lk/ltrl University of Colombo School of Computing - Language Technology Research Lab]

===Taiwan===
*[http://nlg3.csie.ntu.edu.tw/ NTU - CSIE - Natural Language Processing Lab]
*[http://clclp.ling.sinica.edu.tw/ Computational Linguistics and Chinese Language Processing]

===Thailand===
*[http://crcl.th.net/ Center for Research in Computational Linguistics]
*[http://www.crslp.chula.ac.th/ Chulalongkorn University - Center for Research in Speech and Language Processing]
*[http://naist.cpe.ku.ac.th/ Kasetsart University - NAiST-Lab]
*[http://dict.longdo.com/ Longdo Thai-Multilingual Dictionary]
*[http://kind.siit.tu.ac.th/ Sirindhorn International Institute of Technology, Thammasat University - Knowledge Information & Data Management Lab]
*[http://thaispeech.longdo.org/ Thai Speech Community]
*[http://www.tcllab.org/ Thai Computational Linguistics Laboratory]

===Uzbekistan===
*[http://www.elsnet.org/publications/survey/#Uzbekistan Language Engineering in Uzbekistan (from Univ. of Edinburgh/ELSNET)]

==North America==

===Canada===
*[http://www.cs.concordia.ca/CLAC/ Concordia University - Computational Linguistics]
*[http://www.cs.dal.ca/~nlp/ Dalhousie University - Natural Language Processing Group]
*[http://fas.sfu.ca/0/cs/research/groups/NLL/toc.html Simon Fraser University - Natural Language Laboratory]
*[http://www.umanitoba.ca/linguistics/local.html University of Manitoba - Dept. of Linguistics]
*[http://mapageweb.umontreal.ca/dasylval Université de Montréal - Lyne Da Sylva]
*[http://rali.iro.umontreal.ca/ Université de Montréal - Laboratoire de Recherche Appliquée en Linguistique Informatique (RALI)]
*[http://www.csi.uottawa.ca/dept/kaml/KAML.html University of Ottawa - Knowledge Acquisition and Machine Learning (KAML) Group]
*[http://www.cs.toronto.edu/compling/ University of Toronto - Computational Linguistics Group]

===Mexico===

*[http://www.iling.unam.mx Universidad Nacional Autonoma de Mexico - Grupo de Ingenieria Linguistica]
*[http://nlp.cic.ipn.mx National Polytechnic Institute - Center for Computing Research - Natural Language and Text Processing Laboratory]

===USA===
*[http://www.ims.uni-stuttgart.de/info/SitesNorthAmerica.html Computational Linguistics in North America]
====California====
*[http://www-rohan.sdsu.edu/dept/linguist/index.html San Diego State University - Dept. of Linguistics]
*[http://www-cs.stanford.edu/ Stanford University - Computer Science Department]
*[http://www-csli.stanford.edu/ Stanford University - CSLI]
*[http://www-linguistics.stanford.edu/ Stanford University - Linguistics Department]
*[http://symsys.stanford.edu/ Stanford University - Symbolic Systems]
*[http://crl.ucsd.edu/ University of California, San Diego - Center for Research in Language]
*[http://liso.ucsb.edu/ University of California, Santa Barbara - Language, Interaction, and Social Organization Group]
*[http://ling.ucsc.edu/ University of California, Santa Cruz - Linguistics Dept.]
*[http://www.humnet.ucla.edu/humnet/linguistics/general/linguist.htm University of California, Los Angeles - Dept. of Linguistics]
*[http://www.isi.edu/natural-language/nlp-at-isi.html USC - NLP group]
*[http://www.isi.edu/ USC - Information Sciences Institute (ISI)]
*[http://www.ict.usc.edu/ USC - Institute for Creative Technologies]

====Connecticut====
*[http://www.cis.yale.edu/linguist/ Yale University - Department of Linguistics]
*[http://www.haskins.yale.edu/ Yale University - Haskins Laboratories]

====Delaware====
*[http://www.ling.udel.edu/ling/ University of Delaware - Department of Linguistics]
*[http://www.asel.udel.edu/natlang/nli.html University of Delaware - Natural Languages Interface Group]
*[http://www.asel.udel.edu/natlang/nlp/nlp.html University of Delaware - Natural Language Processing Lab]
*[http://www.cis.udel.edu/~decker/ai_nlp.html University of Delaware - Natural Language Processing]

====Illinois====
*[http://l2r.cs.uiuc.edu/~cogcomp/ University of Illinois, Urbana Champaign - Cognitive Computation Group at CS dept]
*[http://www.siu.edu/departments/cola/ling01 Southern Illinois University at Carbondale - Department of Linguistics]
*[http://www.cas.northwestern.edu/linguistics/ Nothwestern University - Department of Linguistics, Weinberg College of Arts and Sciences]
*[http://ap-www.uchicago.edu/AcaPubs/GradAnno/HumDiv/Lingf.html University of Chicago - Dept. of Linguistics]

====Indiana====
*[http://www.indiana.edu/~lingdept Indiana University - Department of Linguistics]

====Iowa====
*[http://www.uiowa.edu/~linguist/ University of Iowa - Department of Linguistics]

====Maryland====
*[http://www.umiacs.umd.edu/labs/CLIP University of Maryland - Computational Linguistics and Information Processing Lab]
*[http://www.clsp.jhu.edu Johns Hopkins University - Center for Language and Speech Processing]
*[http://www.cog.jhu.edu/index.html Johns Hopkins University - Dept. of Cognitive Science]
*[http://nlp.cs.jhu.edu/nlp Johns Hopkins University - Natural Language Processing group]
*[http://www.usna.edu/LangStudy/ US Naval Academy - Language Studies Department]

====Massachusetts====
*[http://das-www.harvard.edu/aiken.html Harvard University - Computer Science Department]

====Michigan====
*[http://ai.eecs.umich.edu/ University of Michigan - AI Lab]
*[http://www.lsa.umich.edu/eli/research/library/ University of Michigan - English Language Institute Library]

====Minnesota====
*[http://carla.acad.umn.edu/ CARLA: Center for Advanced Research on Language Acquisition]

====New Jersey====
*[http://www.tcnj.edu/~mmmartin/CMSC485/CMSC485.html The College of New Jersey - CMSC 485: Special Topics : Question Answering Systems]

====New York====
*[http://www.cs.columbia.edu/nlp/ Columbia University - NLP Group]
*[http://www.cs.columbia.edu/nlp/sgd/ Columbia University - Statistical Generation Day]
*[http://www.cs.cornell.edu/Info/Projects/NLP/ Cornell University - Natural Language Processing, Computer Science]
*[http://web.gc.cuny.edu/linguistics/ CUNY (City University of New York) - Graduate Center Linguistics Program]
*[http://cs.nyu.edu/cs/projects/proteus New York University - Proteus Project]
*[http://www.nyu.edu/pages/linguistics New York University - Linguistics Department]
*[http://www.cs.buffalo.edu/ SUNY Buffalo - Department of Computer Science]
*[http://www.cs.rochester.edu/ University of Rochester - CS Department]
*[http://www.ling.rochester.edu/ University of Rochester - Linguistics department]

====New Mexico====
*[http://crl.nmsu.edu/Home.html New Mexico State University - Computing Research Lab]

====North Carolina====
*[http://www.ils.unc.edu/~losee University of North Carolina - Bob Losee]

====Ohio====
*[http://cllt.osu.edu/ Computational Linguistics and Language Technology at OSU]
*[http://www.ling.ohio-state.edu/research/comp/ The Ohio State University - Computational Linguistics]
*[http://www.cse.ohio-state.edu/slate/ SLATE Lab Speech and Language Technologies at OSU]

====Oregon====
*[http://www.cse.ogi.edu/CHCC Oregon Graduate Institute - Center for Human-Computer Interaction]
*[http://www.cse.ogi.edu/CSLU/ Oregon Graduate Institute - Center for Spoken Language Understanding]

====Pennsylvania====
*[http://www.lti.cs.cmu.edu/ Carnegie Mellon University - Language Technologies Institute]
*[http://www.speech.cs.cmu.edu/ Carnegie Mellon University - Speech Webpage (Sphinx Group)]
*[http://www.cs.cmu.edu/afs/cs/project/soar/public/www/home-page.html Carnegie Mellon University - The SOAR project]
*[http://www.cis.upenn.edu/~ircs/homepage.html University of Pennsylvania - Institute for Research in Cognitive Science]
*[http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html University of Pennsylvania - Research in the Language, Information and Computation Laboratory]
*[http://ling.upenn.edu/ University of Pennsylvania - Dept. of Linguistics]
*[http://www.isp.pitt.edu/ University of Pittsburgh - Intelligent Systems Program]

====Rhode Island====
*[http://www.cog.brown.edu/ Brown University - Department of Cognitive and Linguistic Sciences]
*[http://bllip.cs.brown.edu/ Brown Laboratory for Linguistic Information Processing (BLLIP)]

====Texas====
*[http://www.cs.utexas.edu/users/mfkb/papers University of Texas, Austin - KBS Group, Selected Publications]
*[http://www.utexas.edu/cola/centers/lrc/ University of Texas, Austin - Linguistics Research Center (LRC)]
*[http://www.cs.utexas.edu/users/mfkb/knight.html University of Texas, Austin - NLG in Computer Science]
*[http://comp.ling.utexas.edu University of Texas, Austin - Computational Linguistics Lab]
*[http://ling.uta.edu/ University of Texas, Arlington - Linguistics Dept.]
*[http://www.hlt.utdallas.edu University of Texas, Dallas - Human Language Technology Research Institute]

====Utah====
*[http://nlp.cs.byu.edu/mediawiki/index.php/Main_Page Brigham Young University - Natural Language Processing Lab]
*[http://www.cs.utah.edu/projects/nlp/ University of Utah - Natural Language Processing Group]

====Washington====
*[http://depts.washington.edu/uwcl/ University of Washington - Computational Linguistics Laboratory]
*[http://www.compling.uw.edu/ University of Washington - Computational Linguistics MA program]
*[http://depts.washington.edu/lingweb University of Washington - Dept of Linguistics]
*[http://turing.cs.washington.edu/ University of Washington - Turing Center]
*[http://ssli.ee.washington.edu/ssli/ University of Washington - Signal, Speech and Language Interpretation Lab]

====Washington D.C.====
*[http://www.georgetown.edu/departments/linguistics/news/CLIPos.htm Georgetown University - Department of Linguistics]
*[http://summerschool.georgetown.edu/ Georgetown University - Summer Institute]
*[http://www.georgetown.edu/compling/home.html Georgetown University - Department of Linguistics]

====Wisconsin====
*[http://tigger.cs.uwm.edu/~nlkrrg University of Wisconsin, Milwaukee - Natural Language and Knowledge Representation Research Group]

==Europe==
*[http://www.ims.uni-stuttgart.de/info/SitesEurope.html Computational Linguistics in Europe]
*[http://www.coli.uni-saarland.de/msc/lct/ European Masters Program in Language and Communication Technologies (LCT)]
*[http://www.coli.uni-sb.de/egk/ European Post-Graduate College Language Technology and Cognitive Systems]
*[http://ixa.si.ehu.es IXA Group, Kepa Sarasola]
*[http://www.langsoft.ch NLP software]
*[http://www.shef.ac.uk/dcs/postgrad/taught/hlt.html University of Sheffield - Department of Computer Science]

===Belarus===
*[http://www.elsnet.org/publications/survey/#Belarus Language Engineering in Belarus (from Univ. of Edinburgh/ELSNET)]
===Belgium===
*[http://www.fltr.ucl.ac.be/fltr/germ/etan/cecl/cecl.html UCLouvain - Centre for English Corpus Linguistics (CECL)]
*[http://cental.fltr.ucl.ac.be/ UCLouvain - Centre for Natural Language Processing (CENTAL)]
*[http://www.ccl.kuleuven.be/ Katholieke Universiteit Leuven - Centre for Computational Linguistics]

===Bulgaria===
*[http://www.bacl.org/ Bulgarian Association for Computational Linguistics]
*[http://www.elsnet.org/publications/survey/#Bulgaria Language Engineering in Bulgaria (from Univ. of Edinburgh/ELSNET)]

===Croatia===
*[http://www.ihjj.hr/ Institute of Croatian Language and Linguistics]
*[http://ling.unizd.hr/ University of Zadar - Linguistics Department]
*[http://www.ffzg.hr/zzl/ University of Zagreb - Institute of Linguistics, Faculty of Philosophy]
*[http://www.ffzg.hr/oling/ University of Zagreb - Department of Linguistics, Faculty of Philosophy]
*[http://www.elsnet.org/publications/survey/#Croatia Language Engineering in Croatia (from Univ. of Edinburgh/ELSNET)]

===Czech Republic===
*[http://www.cuni.cz/ Univerzita Karlova] (Charles University) [http://en.wikipedia.org/wiki/Prague Praha]
**[http://www.mff.cuni.cz/ Matematicko-fyzikální fakulta] (Faculty of Mathematics and Physics)
***[http://ufal.mff.cuni.cz/ Ústav formální a aplikované lingvistiky] (Institute of Formal and Applied Linguistics)
**[http://www.ff.cuni.cz/ Filozofická fakulta] (Faculty of Philosophy and Arts)
***[http://utkl.ff.cuni.cz/ Ústav teoretické a komputační lingvistiky] (Institute of Theoretical and Computational Linguistics)
***[http://ucnk.ff.cuni.cz/ Ústav Českého národního korpusu] (Institute of the Czech National Corpus)
*[http://www.cas.cz/ Česká akademie věd] (Czech Academy of Sciences)
**[http://www.ujc.cas.cz/ Ústav jazyka českého] (Institute of the Czech Language)
*[http://www.muni.cz/ Masarykova univerzita] (Masaryk University) [http://en.wikipedia.org/wiki/Brno Brno]
**[http://www.fi.muni.cz/ Fakulta informatiky] (Faculty of Computer Science)
***[http://nlp.fi.muni.cz/ Laboratoř zpracování přirozeného jazyka] (Natural Language Processing Laboratory)
*[http://www.zcu.cz/ Západočeská univerzita] (University of West Bohemia) [http://en.wikipedia.org/wiki/Pilsen Plzeň]
**[http://www.fav.zcu.cz/ Fakulta aplikovaných věd] (Faculty of Applied Science)
***[http://www.kky.zcu.cz/ Katedra kybernetiky] (Department of Cybernetics)
***[http://www.kiv.zcu.cz/ Katedra informatiky a výpočetní techniky] (Department of Computer Science and Engineering)
*[http://www.elsnet.org/publications/survey/ Language Engineering in the Czech Republic (from Univ. of Edinburgh/ELSNET)]

===Denmark===
*[http://www.cst.dk/ University of Copenhagen - Center for Sprogteknologi (Centre for Language Technology)]
*[http://www.cphling.dk/ University of Copenhagen - Linguistics Department]
*[http://isle.nis.sdu.dk/ NIMM workgroup on Natural Interaction and MultiModality]

===Estonia===
*[http://www.elsnet.org/publications/survey/#Estonia Language Engineering in Estonia (from Univ. of Edinburgh/ELSNET)]

===Finland===
*[http://www.ling.helsinki.fi/ University of Helsinki - Department of General Linguistics]

===France===
*[http://lia.univ-avignon.fr/fileadmin/axes/TALNE/ Université d'Avignon - LIA TALNE | NLP ]
*[http://lia.univ-avignon.fr/thematiques/langage/index.html?L=lctajrdpfjmaan Université d'Avignon - Laboratoire Informatique d'Avignon (LIA) : Language]
*[http://www.limsi.fr/Recherche/LIR/presentationLIRgb.html Limsi, group LIR: Language, Information and Representation]
*[http://www.med.univ-rennes1.fr/menelas.html University of Rennes - MENELAS PROJECT]
*[http://infolingu.univ-mlv.fr/english/International/postDocResources.html Postdoctoral Fellowship at Institut Gaspard-Monge]
*[http://www.linguist.jussieu.fr/ UFRL - Linguistics, Jussieu, Paris]
*[http://talc.loria.fr/-Enseignement-.html Two NLP masters at University of Nancy (in English and French)]

===Georgia===
*[http://www.elsnet.org/publications/survey/#Georgia Language Engineering in Georgia (from Univ. of Edinburgh/ELSNET)]

===Germany===
*[http://mm-werkstatt.informatik.uni-augsburg.de/ University of Augsburg - Multimedia Concepts and Their Applications]

*[http://hpsg.fu-berlin.de/ Freie Universität Berlin (FU Berlin) - German Grammar and General Linguistics] (HPSG group)

*University of Bielefeld (Universität Bielefeld)
**[http://www.cit-ec.de/ Center of Excellence in Cognitive Interaction Technology] (CITEC)
**[http://coli.lili.uni-bielefeld.de/ Dept. of Applied Computational Linguistics and Text Technology]
**[http://wwwhomes.uni-bielefeld.de/mkracht/index-en.html Dept. of Theoretical Computational Linguistics and Mathematical Linguistics]
**[http://www.uni-bielefeld.de/lili/forschung/ag_fachber/theoling/ Dept. of Theoretical Linguistics]
**[http://www.techfak.uni-bielefeld.de/ags/wbski/wbski_engl.html Artificial Intelligence Group]
**[http://www.techfak.uni-bielefeld.de/ags/soa/ Sociable Agents Group]
**[http://aiweb.techfak.uni-bielefeld.de/ Applied Computer Science Group]

*[http://www.linguistics.ruhr-uni-bochum.de/ University of Bochum - Dept. of General and Comparative Linguistics]

*[http://www-user.uni-bremen.de/~bateman/ University of Bremen - English/Computational Linguistics]

*[http://www.ukp.tu-darmstadt.de/ Darmstadt University - Ubiquitous Knowledge Processing Group, Dept. of Telecooperation]

*[http://www.dfki.de/ DFKI] - German Research Center for Artificial Intelligence (Kaiserslautern, Saarbrücken, Bremen, Berlin)

*[http://pi7.fernuni-hagen.de/ University Hagen - Dept. of Artificial Intelligence and Natural Language Processing]

*University of Heidelberg
**[http://www.cl.uni-heidelberg.de/ Computational Linguistics]
**[http://www.gs.uni-heidelberg.de/ Germanistisches Seminar] (visiting profs wanted)

*[http://www.ids-mannheim.de/ Institut fuer deutsche Sprache] (Mannheim)

*[http://www.uni-koblenz.de/~compling University of Koblenz - Institute for Computational Linguistics]

*[http://ling.uni-konstanz.de/ University of Konstanz - Linguistics dept.]

*[http://www.asv.informatik.uni-leipzig.de/index.php?lan=en University of Leipzig - NLP Department]

*University of Munich (LMU München)
**[http://www.phonetik.uni-muenchen.de/Bas/BasHomeeng.html Bayerisches Archiv für Sprachsignale]
**[http://www.phonetik.uni-muenchen.de/ Phonetics Department]

*[http://www.phil.uni-passau.de/linguistik/ University of Passau - Linguistics dept.]

*[http://www.ling.uni-potsdam.de/ University of Potsdam - Linguistics dept.]

*Saarland University (Saarbrücken)
**[http://www.coli.uni-saarland.de/ Dept. of Computational Linguistics and Phonetics]
**[http://www.lsv.uni-saarland.de/ Dept. of Spoken Language Systems]

*University of Stuttgart
**[http://www.ims.uni-stuttgart.de/ Institute for Natural Language Processing]
**[http://www.ims.uni-stuttgart.de/projekte/gramotron/ IMS Theoretical Computational Linguistics: GRAMOTRON Group]

*[http://www.ldv.uni-trier.de/ University of Trier - Computational Linguistics]

*[http://www.sfs.nphil.uni-tuebingen.de/ University of Tübingen - Dept. of Linguistics]

===Greece===
*[http://nlp.cs.aueb.gr/ Athens University of Economics and Business, Department of Informatics, Natural Language Processing Group]
*[http://www.ilsp.gr/ Institute for Language and Speech Processing, Athens]
*[http://glotta.ntua.gr/nlp Natural Language Processing Lab, NTUA, Greece]
*[http://www.iit.demokritos.gr/skel Software and Knowledge Engineering Lab, NCSR "Demokritos", Athens, Greece]
*[http://slt.wcl.ee.upatras.gr/ U. of Patras - Speech and Language Technology group]

===Hungary===
*[http://www.elsnet.org/publications/survey/#Hungary Language Engineering in Hungary (from Univ. of Edinburgh/ELSNET)]

===Iceland===
*[http://tungutaekni.is/english.html University of Iceland - Icelandic Centre for Language Technology]

===Ireland===
*[http://www.cs.tcd.ie/courses/csll/CSLLcourse.html University of Dublin - Trinity College - Undergraduate Degree in Computational Linguistics]

===Italy===
*[http://www.ciscl.unisi.it/ University of Siena - Centro Interdipartimentale di Studi Cognitivi sul Linguaggio (CISCL)]
*[http://tcc.itc.it/ ITC-Irst - Cognitive and Communication Technologies division]
*[http://www.ilc.pi.cnr.it/ ILC - Istituto di Linguistica Computazionale]
*[http://lcl.di.uniroma1.it/ Linguistic Computing Laboratory - University of Rome La Sapienza]

===Latvia===
*[http://www.elsnet.org/publications/survey/#Latvia Language Engineering in Latvia (from Univ. of Edinburgh/ELSNET)]

===Lithuania===
*[http://www.elsnet.org/publications/survey/#Lithuania Language Engineering in Lithuania (from Univ. of Edinburgh/ELSNET)]

===Netherlands===
*[http://ilk.uvt.nl/~antalb/ Tilburg University - Antal van den Bosch, Computational Linguistics and AI]
*[http://www.let.rug.nl/~vannoord/Clin/clin.html CLIN - Computational Lingustics in the Netherlands]
*[http://wwwseti.cs.utwente.nl/Docs/parlevink/parlevink.html Univ. of Twente - Parlevink Linguistic Engineering Project]
*[http://fonsg3.let.uva.nl/ University of Amsterdam - Institute of Phonetic Sciences]
*[http://www.hum.uva.nl/graduateschool University of Amsterdam - Masters Program in Linguistics]
*[http://www.cs.kun.nl/agfl/ University of Nijmegen - Affix Grammars over a Finite Lattice (AGFL)]
*[http://www-uilots.let.uu.nl/ Utrecht University - Institute of Linguistics OTS]

===Norway===
*[http://multilingua.uib.no/ University of Bergen - MULTILINGUA]
*[http://www.mn.uio.no/ifi/english/research/groups/ltg/ University of Oslo - Language Technology Group (LTG)]
*[http://giellatekno.uit.no/ University of Tromsø - Sámi language technology]

===Poland===
*[http://www.elsnet.org/publications/survey/#Poland Language Engineering in Poland (from Univ. of Edinburgh/ELSNET; outdated!)]
*[http://nlp.ipipan.waw.pl/ Polish Academy of Sciences - Language Engineering Group]

===Romania===
*[http://www.elsnet.org/publications/survey/#Romania Language Engineering in Romania (from Univ. of Edinburgh/ELSNET)]

===Russia===
*[http://rykov-cl.narod.ru/ Corpus Linguistics - course of lectures read in Grodno in 2002]

===Slovakia===
*[http://www.elsnet.org/publications/survey/#Slovak Republic Language Engineering in Slovakia (from Univ. of Edinburgh/ELSNET)]

===Slovenia===
*[http://kt.ijs.si/ Jozef Stefan Institute - Department of Knowledge Technologies]
*[http://www.elsnet.org/publications/survey/#Slovenia Language Engineering in Slovenia (from Univ. of Edinburgh/ELSNET)]

===Spain===
*[http://www.grupocole.org/ COLE Research Group]
*[http://www.deli.deusto.es Deusto University - DELi: Digital edition and language engineering]
*[http://www.upf.es/dtf Universitat Pompeu,Fabra (UPF), Barcelona - Department of Translation and Philology]
*[http://www.dq-salamanca.com don Quijote Salamanca Spanish Language School]
*[http://protos.dis.ulpgc.es Universidad de Las Palmas de Gran Canaria - Grupo de Estructuras de Datos y Linguistica Computacional]
*[http://www.iiia.csic.es/ Institut d'Investigació en Intelligència Artificial]
*[http://www.lllf.uam.es/ Universidad Autonoma de Madrid - Laboratorio de Linguistica Informatica (LLI-UAM)]
*[http://www.um.es/lacell/ University of Murcia - LACELL Research Group]
*[http://gplsi.dlsi.ua.es/ University of Alicante - Natural Language Processing Research Group]
*[http://transducens.dlsi.ua.es/ University of Alicante - Transducens Group]
*[http://www.lsi.upc.es/~nlp/ Technical University of Catalonia - Natural Language Research Group]
*[http://sli.uvigo.es Universidade de Vigo - Seminario de Linguistica Informatica (SLI)]
*[http://www.talp.upc.es/ TALP Research Center]
*[http://www.vai.dia.fi.upm.es/ Validation and Bussiness Applications Group. Linguistic team.]
*[http://sinai.ujaen.es/ University of Jaén - SINAI Research Group -]
*[http://ixa.si.ehu.es/ University of the Basque Country - IXA Group]
*[http://aholab.ehu.es University of the Basque Country - Aholab group]

===Sweden===
*[http://www.ling.gu.se/ Univ. of Goteborg - Department of Linguistics]
*[http://www.speech.kth.se/ KTH - Department of Speech Communication and Music Acoustics]
*[http://www.ling.lu.se/ Lund University - Dept. of Linguistics and Phonetics]
*[http://www.ida.liu.se/~nlplab/ Linköping University - NLPLAB, Department of Computer and Information Science]
*[http://www.ling.gu.se/~cooper Robin Cooper]
*[http://www.sics.se/nlp/nlp.html SICS - Natural Language Processing Group]
*[http://www.ling.uu.se/ University of Uppsala - Linguistics]
*[http://stp.ling.uu.se/stp/ University of Uppsala - STP: Language Engineering Programme]
*[http://jean.ling.umu.se/ Umea University - Institute of Linguistics]

===Switzerland===
*[http://www.ifi.unizh.ch/groups/CL/ Univ. of Zurich - Computational Linguistics Group, Dept. of Computer Science]
*[http://www.unifr.ch/ l'Universite de Fribourg - Institut d'Informatique]
*[http://www.idiap.ch/ Institut Dalle Molle d'Intelligence Artificielle Perceptive]
*[http://www.unil.ch/ling/Bienvenue.html Univ. de Lausanne - Linguistics Department]
*[http://www.unil.ch/ling UNIL - Linguistique]
*[http://www.issco.unige.ch Multilingual Information Processing Unit (TIM), School of Translation and Interpretation (ETI), University of Geneva]
*[http://www.latl.unige.ch LATL - Language Technology Laboratory, Department of Linguistics, University of Geneva]

===Turkey===
*[http://ai.ku.edu.tr Koç University - Artificial Intelligence Laboratory]
*[http://www.lcsl.metu.edu.tr/ Middle East Technical University - Laboratory for the Computational Studies of Language]
*[http://www.hlst.sabanciuniv.edu Sabanci Univ. - Human Language and Speech Technologies Labratory]

===UK===
*[http://www.csd.abdn.ac.uk/research/nlg/ University of Aberdeen - Natural Language Generation Group]
*[http://www.csd.abdn.ac.uk/research/babytalk/ University of Aberdeen - Babytalk: Generating Textual Summaries of Clinical Temporal Data]
*[http://www.cl.cam.ac.uk/Research/NL/index.html University of Cambridge - NLP group]
*[http://svr-www.eng.cam.ac.uk/cstit/ University of Cambridge - Computer speech, text, and internet technology]
*[http://rdues.uce.ac.uk/ University of Central England - Research and Development Unit for English Studies (RSDUES)]
*[http://www.dai.ed.ac.uk/ University of Edinburgh - Department of Artificial Intelligence]
*[http://www.cogsci.ed.ac.uk/ccs/home.html University of Edinburgh - Alexander Holt, Centre for Cognitive Science]
*[http://www.cogsci.ed.ac.uk/hcrc/home.html University of Edinburgh - Human Communication Research Center]
*[http://www.essex.ac.uk/linguistics/ University of Essex - Department of Linguistics]
*[http://cswww.essex.ac.uk/LAC/ University of Essex - Language and Computation Group]
*[http://clwww.essex.ac.uk/ University of Essex - CL/MT Research Group Home Page]
*[http://www.dcs.kcl.ac.uk/research/groups/nlp/index.html King's College - MSc and MPhil/PhD Programs in NLP in the Computer Science Dept.]
*[http://mcs.open.ac.uk/nlg/ The Open University - Natural Language Generation group, Centre for Research in Computing]
*[http://www.clg.ox.ac.uk Oxford University - CL group]
*[http://www.comp.lancs.ac.uk/computing/research/ucrel/corpora.html Lancaster University - UCREL corpus page]
*[http://www.comp.lancs.ac.uk/computing/research/ucrel/ Lancaster University - Unit for Computer Research on the English Language]
*[http://www.comp.leeds.ac.uk/nlp/ University of Leeds - School of Computing, NLP group]
*[http://www.phon.ucl.ac.uk/ Univ. of London - University College Dept. of Phonetics and Linguistics]
*[http://osiris.sunderland.ac.uk/jta/nl-grp.htm University of Sunderland - Natural Language Engineering Group]
*[http://www.surrey.ac.uk/ELI/eli.htm University of Surrey - English Language Institute]
*[http://www.cogs.susx.ac.uk/lab/nlp/index.html University of Sussex - Computational Linguistics]
*[http://clg.wlv.ac.uk/ University of Wolverhampton - Computational Linguistics]

===Ukraine===
*[http://www.elsnet.org/publications/survey/#Ukraine Language Engineering in Ukraine (from Univ. of Edinburgh/ELSNET)]

==Oceania==
*[http://www.clt.mq.edu.au/ Macquarie University - Centre for Language Technology]
*[http://www.ims.uni-stuttgart.de/info/SitesAustralia.html Computational Linguistics in Australia]
*[http://www.vuw.ac.nz/ling Victoria University of Wellington, New Zealand - Department of Linguistics]
*[http://www.auckland.ac.nz/asi/asi_home.html The University of Auckland, New Zealand - Dept. of Asian Languages & Literatures]
*[http://www.cs.mu.oz.au/research/lt/ University of Melbourne - Language Technology Group, Department of Computer Science and Software Engineering]
*[http://www.cs.usyd.edu.au/~rcdmnl/ University of Sydney - Language Technology Research Group, School of Information Technologies]
*[http://www.arts.unimelb.edu.au/Dept/LALX University of Melbourne - Department of Linguistics & Applied Linguistics]
*[http://www.csse.monash.edu.au/research/umnl/ Monash University - User Modeling and Natural Language Group, ]

==South America==

===Brazil===
*[http://www.di.ufpe.br/ Federal University of Pernambuco - Jacques Robin, CS Department]
*[http://www.nilc.icmc.usp.br/nilc/ NILC - An Interinstitutional Center for Research and Development in Computational Linguistics]

==See Also==
*[[Organizations, departments, institutions, groups, companies]] - full list of organizations that do CL

[[Category:Education]]

Employment opportunities, postdoctoral positions, summer jobs

2009-09-08T16:27:16Z

Oe: Associate Professorship at University of Oslo

* Please post your job ad below.
* Jobs are listed in chronological order of posting: '''first is newest, last is oldest'''.
* '''Please remove your posting when the position is filled.'''
* Please include the following information:
** Employer
** Rank or Title
** Specialty (e.g., Computational Linguistics, Natural Language Processing, Machine Translation)
** Location
** Deadline
** Date Posted
** Contact email or link to website
* See also the [http://linguistlist.org/jobs/index.html Linguist Job List].
* Archived postings:
** [[Employment opportunities posted 2008]]
** [[Employment opportunities posted 2007]]

== Associate Professorship in Natural Language Processing, University of Oslo ==

* '''Employer:''' [http://www.ifi.uio.no Department of Informatics], [http://www.uio.no University of Oslo]
* '''Title:''' Associate Professor (tenured)
* '''Specialization:''' data-driven and hybrid approaches to NLP
* '''Deadline:''' Friday, October 9, 2009
* '''Announcement:''' [http://www.emmtee.net/ap.09.09.html job posting]

== Text Mining Positions, JULIE Lab (Germany) ==

* '''Employer:''' JULIE Lab, Friedrich-Schiller-Universität Jena, Germany
* '''Title:''' Post doc or Ph.D.
* '''Specialty:''' NLP
* '''Deadline:''' Open until filled
* '''Date Posted:''' Aug 26, 2009
* '''Contact:''' Katrin Tomanek <katrin.tomanek@uni-jena.de>

* '''Links to website:''' [http://www.julielab.de JULIE Lab Web Site]

The Jena University Language & Information Engineering (JULIE) Lab has two vacant text mining positions available, a Post Doc and a Ph.D. position, to boost its research efforts in advanced text analytics, i.e., named entity and relation extraction, knowledge discovery from texts. Annual salary ranges from 40,000 – 45,000 € (approximately 60,000 $) for the post doc position and 35,000 – 40,000 € (approximately 55,000 $) for the Ph.D. position, both dependent on previous work experience. JULIE Lab has a strong reputation in biomedical information extraction and text mining and is an international leader in research in the field of text analytics (please, visit http://www.julielab.de).

Requirements for Candidates:

* outstanding degree in computational linguistics/language technology or related fields in computer science (information retrieval, data mining, artificial intelligence, machine learning, information systems, human-computer interaction, Semantic Web, etc.) from a major school/department/university
* necessary: good proficiency in statistics and machine learning methodologies
* desired: solid information extraction/text mining skills
* excellent programming skills (Java), awareness of software engineering discipline in coding (UIMA, coding conventions, SE tools, etc.)
* background knowledge in the life sciences (can also be acquired or deepened while staying in the lab)
* strong dedication to scientific excellence

Informal inquiries can be addressed to:
Katrin Tomanek, Friedrich-Schiller-Universität Jena, Email: katrin.tomanek@uni-jena.de

Applications (e-mail preferred) should be sent to:
Prof. Dr. Udo Hahn, Friedrich-Schiller-Universität Jena, 07743 Jena, Fürstengraben 30, Germany, Email: udo.hahn@uni-jena.de

== Programmer Analyst, Linguistic Data Consortium, University of Pennsylvania (Philadelphia, PA, USA) ==

* '''Employer:''' Linguistic Data Consortium, University of Pennsylvania
* '''Title:''' Programmer Analyst (Ref #: 090727106)
* '''Specialty:''' Language Resource Creation, Natural Language Processing
* '''Deadline:''' Open untill filled
* '''Date Posted:''' Aug 12, 2009
* '''Contact:''' Kazuaki Maeda <maeda@ldc.upenn.edu>

* '''Links to website:''' [https://jobs.hr.upenn.edu/applicants/Central?quickFind=190206 Online Application] [http://www.ldc.upenn.edu/ LDC Web Site]

Linguistic Data Consortium at the University of Pennsylvania has an immediate opening for a full-time programmer analyst to support our
rapidly growing linguistic resource creation projects, including the DARPA Machine Reading program.

Duties: This position will support multiple, concurrent language resource creation projects by providing programming, technical and research support in a lead capacity. Working with internal and external stakeholders, the position will develop achievable technical roadmaps for each project and will oversee their execution.

Tasking may include:

# design and develop flexible user interfaces for manual annotation of linguistic data
# prepare linguistic resources for annotation, including indexing and format normalization
# negotiate project technical specifications with key internal and external stakeholders
# develop data formats to support collection and annotation tasks
# research, evaluate and integrate third-party software into LDC's data production pipeline
# perform statistical analysis of linguistic data
# train and supervise full-time or part-time technical staff
# assist with corpus publication including data validation and sanity checks
# provide technical support to linguistic annotators and other project staff
# represent technical aspects of LDC's linguistic resource creation efforts at workshops and in publications, as required.

This position is contingent upon funding. For further information or to apply online, please go to [https://jobs.hr.upenn.edu/applicants/Central?quickFind=190206 Online Application]. The University of Pennsylvania is an affirmative action/equal opportunity employer.

== Postdoctoral Fellowship, Department of Biomedical Informatics, University of Utah ==

* '''Employer:''' Department of Biomedical Informatics, University of Utah
* '''Title:''' Postdoctoral Fellowship
* '''Specialty:''' Natural Language Processing (machine learning and/or UIMA experience a plus!)
* '''Deadline:''' Open, hope to fill to fill by October 01, 2009
* '''Date Posted:''' July 05, 2009
* '''Contact:'''
Jo Ann Thompson (administrative issues)=> JoAnn.Thompson@hsc.utah.edu;

John Hurdle, MD, PhD (PI)=> john.hurdle@utah.edu
* '''Links to website:''' [http://www.bmi.utah.edu/ UU Biomedical Informatics Website]

Postdoctoral Fellowship in clinical NLP (one year, possibly two)
Salary is based on NIH postdoctoral pay scales (see [http://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-057.html NIH postdoc stipends])

In an effort to improve clinical NLP, the POET project researches ways to preprocess raw clinical text into a form amenable for high-level NLP systems. This postdoctoral fellowship offers an opportunity to play a substantial role in the development of advanced technologies for resolving clinical short forms (e.g., abbreviations and shorthand); to re-write templated text; and to formally explore clinical sub-languages. The recipient of the fellowship, funded under an a grant from the NIH, will be expected to add his/her own research project to the over goals of POET and will be mentored in 1) clinical NLP in a high-performance, clustered computing environment; 2) in preparing manuscripts for publication; 3) and in biomedical informatics grant writing (with a special focus on the NIH K99/R00 grant process).

POET is being built around a UIMA pipeline architecture and is implemented on a small, dedicated, HIPAA-compliant high-performance compute cluster (56 cores and counting…). POET currently has access to >700,000 clinical notes spanning multiple clinical disciplines and clinical settings from two different healthcare networks.

Qualifications: PhD-level experience in natural language processing is a must. An NLP focus in biomedical NLP experience is desired but not mandatory, as is some experience in machine learning techniques. UIMA experience is a definite plus. For more information on POET, query grant number "1R21LM009967-01" at [http://crisp.cit.nih.gov/crisp/crisp_query.generate_screen NIH grant queries]

If you are interested AND if you will have in your PhD in-hand by the time you start the fellowship (can start ASAP, position open till filled), then please send your CV and a statement of research interests to the contact for Jo Ann Thompson above.

The University of Utah is committed to policies of equal opportunity and nondiscrimination. The University pursues a vigorous program of affirmative action in all classifications of employment in order to prevent any form of discrimination, harassment, or prejudicial treatment on the basis of race, color, religion, national origin, sex, age, sexual orientation, status as a disabled individual, disabled veteran, or veteran of the Vietnam Era.

== Research Programmer, University of Illinois, Urbana, Illinois ==

* '''Employer:''' University of Illinois, Urbana, Illinois
* '''Title:''' Research Programmer
* '''Specialty:''' Natural Language Processing, Machine Learning, Web Programming
* '''Deadline:''' July 10, 2009-
* '''Date Posted:''' June 21, 2009
* '''Contact:''' Jennifer Dittmar at 217-244-6241
* '''Links to website:''' [http://www.cs.uiuc.edu/join/aponlap.html]

Visiting Research Programmer
Department of Computer Science
University of Illinois Urbana-Champaign
Extended and Revised Search

The Department of Computer Science is seeking a Visiting Research Programmer to take part in the development of Natural Language Processing and Machine Learning technologies embedded in a Web-based application.

This position requires knowledge in one or more of the following: information retrieval techniques and tools, natural language processing, language models, machine learning techniques, and web programming (e.g., browsers plug-ins). Algorithmic maturity and experience dealing with large amounts of data are essential. Outstanding attention to detail is required, along with strong written and verbal communication skills and the ability to quickly learn new technologies. The Visiting Research Programmer will join the Cognitive Computation Group led by Prof. Dan Roth, to work on a standalone project with possibilities for commercialization.

Job Duties include:

* Designing and implementing algorithms, software systems and user interfaces
* Writing design documents and code documentation
* Interacting with other group members and presenting technical information

A minimum of a Bachelor's degree in Computer Science or a related field is required. Individuals with a more advanced degree are encouraged to apply and salary will be commensurate with level of education and experience. This position will be available as soon as possible after the closing date, and is a 12-month, temporary, full-time academic professional appointment with standard University benefits with the possibility of becoming permanent at a future date. Salary is commensurate with education and experience. For full consideration, applications should be received by 7/10/09. Applicants may be interviewed before the closing date; however, no hiring decision will be made until after that date.

To apply, please submit your application online at: http://www.cs.uiuc.edu/join/aponlap.html.

If you do not have Internet access, please call Jennifer Dittmar at 217-244-6241 to make other arrangements for submitting your application. Refer to search #12309 in all communications.

Minorities, women, and other designated class members are encouraged to apply.

The University of Illinois is an Affirmative Action-Equal Opportunity Employer.

== Postdoc or doctoral research assistant, Bremen University, Bremen, Germany ==

* '''Employer:''' Bremen University, Bremen, Germany
* '''Title:''' Research assistant (Postdoc or doctoal level)
* '''Specialty:''' Dialogue Systems and Human-Robot Interaction
* '''Deadline:''' June 15, 2009
* '''Date Posted:''' May 17, 2009
* '''Contact:''' John Bateman <bateman@uni-NOSPAM-bremen.de> [remove 6 chars]
* '''Links to website:''' [http://www.diaspace.uni-bremen.de | Research Position]

The I5-DiaSpace project of the Collaborative Research Center on
Spatial Cognition in Bremen, N. Germany, is looking for a
computational linguistic or informatics PhD or Postdoc researcher
ready and willing to take on the further development of our
information-state based dialogue system. The system backbone is
implemented in Java with plug-ins to state of the art natural language
analysis, generation, dialogue management, and knowledge
representation components. The system offers an outstanding foundation
for carrying out further research at both doctoral and postdoc
levels. The successful applicant will be expected to take over
responsibility for system design and implementation, and so excellent
programming skills will be crucial. The selection of research topic
within the general area of dialogue systems is open to negotiation,
although spatially-situated dialogue with autonomous systems including
robots is one of the demonstration areas that we cover. The position
is to be filled as quickly as possible.

The Natural Language Interaction Group at Bremen is a highly
multidisciplinary team combining discourse analysts, psycholinguists,
computational linguists and ontological engineers. We work in close
cooperation with several projects within the Collaborative Research
Center, which spans cognitive science, neurocognition, perception,
formal spatial calculi, architecture, and AI. The environment is
highly international and at the cutting edge of several related
disciplines.

'''Requirements:'''

:• completed first degree in computational linguistics, artificial intelligence, linguistics, informatics or similar
:• interest in dialogue
:• very good programming skills (necessarily Java; Lisp, Perl, etc. advantageous)
:• good English writing skills
:• ability to work both independently and in a team in a multidisciplinary environment

'''Approximate salary:'''

Payment is according to the German Federal pay scale (TV-L 13,
approx. 34,000 Euros annually). The position is full-time.

Interested applicants should send their CV and any supporting
information or further queries to John Bateman at the above contact
address.

== Scientist I, J.D. Power and Associates, Boulder, CO ==

{|
| '''• Employer:''' ||Web Intelligence Division, J.D. Power and Associates (McGraw-Hill)
|-
| '''• Title, Rank:''' ||Scientist I.
|-
| '''• Specialty:''' ||Machine Learning, Computational Linguistics, Natural Language Processing.
|-
| '''• Location:''' ||Boulder, Colorado.
|-
| '''• Start date:''' ||The position is available immediately.
|-
| '''• Date Posted:''' ||May 1st, 2009.
|-
| '''• Links to website:''' ||http://jdpowerwebintelligence.com
|-
| '''• Contact:''' ||Nicolas Nicolov <nicolas_nicolovNOSPAM@jdpa.com> [remove six chars]
|}

J.D.Power and Associates is growing and hiring top-notch scientists to develop cutting-edge web mining technology. Our science team is working on advanced text analysis of vast amounts of data, scalable information retrieval, learning semantic concepts and lots of cool new stuff.

'''Requirements:'''

:• Recent Ph.D. in Machine Learning, Computational Linguistics, Artificial Intelligence, Computer Science or equivalent.
:• Strong statistical, natural language processing, machine learning background with emphasis on coreference, meronymy, dependency parsing, sentiment/opinion analysis, text clustering and categorization, graph analysis.
:• Experience with scientific computing on large datasets, natural language processing, information extraction, information retrieval, multilingual datasets, distributed systems is a plus.
:• Strong experience with C++/Java, Scala/Ruby/Python development of large software systems, Linux/Windows environments.
:• Proven track record of publications/patents preferred.
:• Multilingual processing is a plus.
:• Strong verbal and written communication skills; UML, LaTEX, beamer.
:• At least 4 years of research experience (relevant university experience - ok).
:• Enthusiasm for solving challenging problems.

== Research Position in Computational Linguistics at Austrian Research Institute for AI (OFAI), Vienna ==

* '''Employer:''' OFAI, Vienna, Austria
* '''Title:''' Researcher
* '''Specialty:''' Computational Linguistics
* '''Deadline:''' April 15, 2009
* '''Date Posted:''' March 30, 2009
* '''Links to website:''' [http://www.ofai.at/research/nlu/]

The Austrian Research Institute for Artificial Intelligence (OFAI)
in Vienna, Austria offers two research positions in its Language
Technology group.

Candidates are expected to have a degree in computational linguistics
or computer science with a background in language technology.
She/he will be an open minded team worker, who is able
to work creatively in an interdisciplinary context. She/he will have
good programming skills and be able to flexibly use different
programming languages. She/he likes to to work in a research environment
constantly learning and developing fresh concepts and ideas.
Publishing research results is part of the job and actively encouraged.
The successful candidates will have a strong background in one or more of
the following areas: ontology engineering, semantic systems, text mining
and machine learning.

Fluency in English is expected.

Both positions are in the framework of a project aiming at advancing
speech recognition by the integration of (context-dependent) semantic
knowledge. The project is ongoing and will continue until April 2010.

Contracts will be for the duration of the project.
Continued employment after the end of the project is possible.

Depending on academic credentials and experience of the candidate,
the yearly gross salary will be in the range of EUR 42000 - 54000.

Both positions are to be filled asap.

Please mail applications including a CV to Harald Trost <harald.trost@ofai.at>

==Postdoctoral Fellowship at the Institut de Recherche en Informatique de Toulouse (IRIT) Université de Toulouse 3==

Subject : Applying learning techniques to discourse analysis

Période : 12 months, September 2009 -> August 2010

Context :
This postdoctoral fellowship, is part of the ANR project
ANNODIS, which includes the labs: IRIT (Université de Toulouse 3), CLLE (Université
de Toulouse 2), and GREYC Université de Caen). The goal of this project is to
build a corpus of French texts annotated with discourse structure at several levels. The project also has the goal of providing automatic and semi-automatic tools for helping with this task.

The postdoctoral fellow will be a member of the IRIT lab at l'université de Toulouse 3, Toulouse, in the research group Lilac under the direction of
Nicholas Asher.

Objectives :
Based on the data culled from the manual annotation of our corpus, the first objective of the postdoctoral fellow will be to design and supervise experiments for the automatic recovery of the discourse structure of a text and to evaluate the feasibility of semi supervised and supervised learning strategies given the data in the corpus. A discourse structure is a graph where the nodes are text segments and the arcs are discourse relations. Thus, the extraction task has three stages: 1) finding the segments, 2) determining the attachment points for segments in the graph et 3) determining the discourse relation or relations between the attached segments.

The postdoctoral fellow will also be in charge of the final collection and organization of the manual annotation data.

Candidate should have:
- a Ph.D.
- competence in NLP and/or information extraction, and automated learning methods.

A familiarity with theories of discourse structure would be a Plus.

Salary: 3000 euro per month

Candidates should send a dossier with a detailed CV (pdf) by email to:
asher@irit.fr AND muller@irit.fr.

== Post-Doctoral Position in Computational Linguistics at Uppsala University ==

* '''Employer:''' Uppsala University, Sweden
* '''Title:''' Research Fellow
* '''Specialty:''' Computational Linguistics
* '''Deadline:''' April 24, 2009
* '''Date Posted:''' March 17, 2009
* '''Links to website:''' [http://stp.lingfil.uu.se/~nivre/docs/PostdocUU.pdf | Post-Doc Position]

This is a full-time, limited-term position that can maximally be extended up to four
years. The position involves a small amount of teaching and supervision but is mainly
devoted to research. The deadline for applications is April 14, 2009. For more
information, contact Joakim Nivre <joakim.nivre@lingfil.uu.se>.

== Research Positions, Human Language Technology Center of Excellence ==

* '''Employer:''' Human Language Technology Center of Excellence (at Johns Hopkins University)
* '''Titles:''' Postdoctoral researchers, research staff, professors on sabbaticals, visiting scientists
* '''Specialty:''' Speech and Natural Language Processing
* '''Deadline:''' April 1, 2009
* '''Date Posted:''' March 12, 2009
* '''Links to website:''' [http://web.jhu.edu/HLTCOE/opportunities.html | Research Position]

The Human Language Technology Center of Excellence (COE) at the Johns Hopkins University is seeking to hire outstanding Ph.D. researchers in the field of speech and natural language processing. The COE seeks the most talented candidates for both junior and senior level positions including, but not limited to, full-time research staff, professors on sabbaticals, visiting scientists and post-docs. Candidates will be expected to work in a team setting with other researchers and graduate students at the Johns Hopkins University, the University of Maryland College Park and other affiliated institutions.

Candidates should have a strong background in one of the following areas:

- NATURAL LANGUAGE PROCESSING: Information extraction, knowledge distillation, machine translation, semantic annotation, text processing, etc.

- SPEECH PROCESSING: Robust speech recognition across language channel, formal vs. informal genres, speaker identification, language identification, speech retrieval, spoken term detection, etc.

- MACHINE LEARNING: Learning on very large datasets and streams for text and/or speech, feature extraction, domain adaptation, semi-supervised learning

The COE was founded in January 2007 and has a long-term research contract as an independent center within Johns Hopkins University. Located next to Johns Hopkins’ Homewood Campus in Baltimore, Maryland, the COE’s distinguished contract partners include the University of Maryland College Park, the Johns Hopkins University Applied Physics Lab, and BBN Technologies of Cambridge, Massachusetts. World-class researchers at the COE focus on fundamental challenge problems critical to finding solutions for real-world problems of importance to our government sponsor. The COE offers substantial computing capability for research that requires heavy computation and massive storage. In the summer of 2009, the COE will hold its first annual Summer Camp for Advanced Language Exploration (SCALE), inviting the best and brightest researchers to work on common areas in speech and NLP. Researchers are expected to publish in peer-reviewed venues.

Applicants should have earned a Ph.D. in Computer Science or a closely related field. Applicants should submit a curriculum vitae, research statement, names and addresses of at least four references, and an optional teaching statement. Please send applications and inquiries about the position to hltcoe-hiring@jhu.edu.

While applications will be evaluated as received until the position is filled, applicants are strongly encouraged to indicate intent to apply by contacting the center before April 1, 2009. U.S. Citizenship is required and applicants should note citizenship status on their application. Additionally, security clearance is required and the COE will seek a clearance for those who do not already have one. The Johns Hopkins University is an equal opportunity employer and has a smoke-free environment.

== Chair and Lectureship in Computing Science, Aberdeen ==

* '''Employer:''' University of Aberdeen
* '''Titles:''' Chair (full professor), Lecturer (assistant professor)
* '''Specialty:''' See full adverts. Areas include multi-modal interaction and natural language generation
* '''Deadline:''' April 13, 2009
* '''Date Posted:''' February 26, 2009
* '''Links to website:''' [http://www.abdn.ac.uk/jobs/display.php?recordid=NAT016A | Chair position] [http://www.abdn.ac.uk/jobs/display.php?recordid=NAT017A | Lectureship position] [http://www.csd.abdn.ac.uk/research | Research at Aberdeen]

== Natural Language Generation Group, The Open University: Research Associate, Text-to-Text Generation/Dialogue ==

* '''Employer:''' The Open University
* '''Title:''' Research Associate
* '''Specialty:''' Text-to-Text Generation/Dialogue
* '''Deadline:''' February 15, 2009
* '''Date Posted:''' February 3, 2009
* '''Link to website:''' [http://www3.open.ac.uk/employment/job-details.asp?id=4373&ref=ext| job details and application form]

== National Research Council of Canada: Research Officer, Statistical Semantics ==

* '''Employer:''' National Research Council of Canada
* '''Title:''' Research Officer
* '''Specialty:''' Statistical Semantics (2 positions)
* '''Deadline:''' Posted until filled
* '''Date Posted:''' January 20, 2009
* '''Link to website:''' [http://careers-carrieres.nrc-cnrc.gc.ca/careers/jobpost.nsf/EnglishAll/187241FAD78497FC85257540005E75CA Research Officer, Statistical Semantics (2 positions)]

The Institute for Information Technology at the National Research Council of Canada has openings for two Research Officers to work in the area of statistical semantics, a sub-field of statistical natural language processing. These two positions are full-time and continuing. They are based in Ottawa, Ontario. The successful candidates will perform original research that contributes to the Institute's focus on language processing, text mining, and machine translation. They will, in collaboration with other researchers and programmers, create prototypes of their work and publish their research results in highly-cited journals and conferences. The position offers the opportunity to collaborate with colleagues, university researchers, and industrial partners.

The researchers will work in a results-driven environment and will have the opportunity to apply their research results to ongoing high profile projects, such as processing of textual medical records or performing machine translation.

== Postdoctoral and research engineer positions available at the Singapore Management University ==

The School of Information Systems at the Singapore Management University is seeking to fill a few postdoctoral and research engineer positions for the projects "Transfer Learning for Adaptive Relation Extraction" and "Mining Interaction Behaviors from Information Exchange Networks." These are two-year projects supported by the Singapore Defense Science Organization, starting in April 2009.

The goal of the first project is to develop adaptive learning algorithms for relation extraction from free text. In real applications of relation extraction, there is often insufficient training data available for the target relations in the target domain, but labeled data from related domains or for related relation types can be borrowed. The research questions to be answered are therefore (1) how existing transfer learning algorithms can be applied in the particular context of relation extraction, (2) how human knowledge can be incorporated, and (2) what new transfer learning techniques are needed for adaptive relation extraction.

The goal of the second project is to study characterization and measurement of interaction behaviors in information exchange networks based on user-generated interaction data. We will focus on information exchange networks which involve actors sending information to one another. Examples of such networks include email and blog networks. The research will focus on interaction behaviors that suggest actor roles in an information exchange network and may infer relationships between actors in the network.

The postdoctoral candidate must have completed all requirements for his/her PhD degree by the time of appointment. The research engineer candidate must have completed a good undergraduate or master degree in computer science or computer engineering. The ideal candidates are expected to have the following skills/qualifications:

- Knowledge and experience in machine learning, data mining and statistics/probabilities
- Strong programming skills
- Proficiency in English

For the first project, we also prefer candidates with

- Knowledge and experience in natural language processing
- Experience with information extraction, transfer learning and/or semi-supervised learning is a plus

For the second project, we also prefer candidates with

- Knowledge in social network analysis and web mining

The salary for the postdoctoral position will be around 5,000 SGD per month. The salary for the research engineer position will be based on the candidate’s working experience and academic degree.

To apply for the positions, send your full CV with list of publications, names and contact information of two referees, a statement of research qualifications and interests, and two sample publications (if any) to the following principle investigators:

1. Transfer Learning for Adaptive Relation Extraction: Jing Jiang (jingjiang@smu.edu.sg).
2. Mining Interaction Behaviors from Information Exchange Networks: Ee-Peng Lim (eplim@smu.edu.sg).

== Tenure Track Faculty Position: Assistant/Associate Professor in Human Computer Interaction, Montclair State University, NJ, USA ==

Vacancy #: VF-22
Department: Computer Science

The Department of Computer Science invites applications for a tenure track position in Human Computer Interaction (HCI) and Visualization. The Department’s 13 faculty members support the BS in Computer Science with an ABET CAC accredited track, the BS in Information Technology and the MS in Computer Science. The position requires a willingness to teach a variety of computer science and information technology courses at all levels to ethnically diverse students. The position entails the ability to work as a member of interdisciplinary teams as the Department develops and modifies computing undergraduate and graduate programs with a planned doctoral program in computational science. In addition, the successful candidate will develop and maintain an active research program with student involvement.

Qualifications & Requirements: Candidates must have a Ph.D. in computer Science or a very closely related discipline. Candidates must have expertise in Human Computer Interaction with preference to candidates with experience in software engineering, interfaces, and visualization, and research in HCI. Candidates must have good communication skills. We are looking for candidates with experience in teaching undergraduate computing courses and in working as a member of a team. All faculty are expected to have an ongoing research program, to commit to quality teaching, to be involved in professional activities, and to pursue external funding to support their scholarship.

Salary Range: Salary and range is dependent on qualifications.
Anticipated Start Date: September 1, 2009

Send letter and resume to (include vacancy # if above):
Send hardcopy (no email documents) that includes C.V., at least three professional references, statement of research interests, teaching philosophy with experience, and professional goals to:

Search Committee — V- F22
Department of Computer Science
Montclair State University
Montclair, NJ 07043

(include V number) and professional goals to:
Search Committee — V- F22
Department of Computer Science
Montclair State University
Montclair, NJ 07043

Apply By: Screening begins immediately and continues until the position is filled.

Montclair State is a New Jersey State university. It is located 14 miles west of New York City.

== Internship on Textual Entailment Applied to Statistical Machine Translation, Xerox Research Centre Europe, Grenoble - France ==

The main research lines within the Cross Language Technologies (CLT) area at XRCE are Statistical Machine Translation, Cross-Lingual Information Retrieval and Machine Learning Techniques for Cross-Lingual Applications. CLT is currently coordinating the European Project SMART (Statistical Multilingual Analysis for Retrieval and Translation) [http://www.smart-project.eu].

XRCE has received funding from the PASCAL-2 Network of Excellence [http://pascallin2.ecs.soton.ac.uk] for conducting, in partnership with Bar-Ilan University in Israel, a "Pump Priming" project on the topic of "Context Models for Textual Entailment and their Application to Statistical Machine Translation". One of the goals of the project is to investigate situations in which, while a translation system may not have enough knowledge to adequately translate a source text into a target text, it may be able to do so based on entailment rules learned from monolingual data.

We are looking for preferably one (in this case the internship would be for 10 months ) or two interns (in this case each internship would last 5 months with the first starting at the beginning of 2009) to work on this topic under the supervision of XRCE researchers, and in collaboration with our partners. The focus of the work will be on the following topics:

* Integration of existing paraphrase and entailment resources into SMT settings, and assessment of their applicability in this domain;
* Development (in collaboration with our partners) of novel models for assessing the validity of entailment rules in context and implementation of SMT-based modules that are able to exploit such rules;
* Methodology and measures for controlling the use of directional and bi-directional entailment rules in SMT;
* Use of entailment knowledge for evaluating the performance of SMT systems.

The ideal candidate will be a strong Master or Ph.D. student with background in statistical machine translation and/or statistical methods in NLP. The candidate will be fluent in C/C++ and/or Python. Some knowledge and practice of Machine Learning models and tools will be a plus, as will be some previous acquaintance with work on Textual Entailment.

Contact:

* Lucia Specia: lucia.specia@xrce.xerox.com
* Marc Dymetman: marc.dymetman@xrce.xerox.com

For more information: http://www.xrce.xerox.com/internships/LS-MD.TE-SMT_2009.2008.html