@inproceedings{schmidek-barbosa-2014-improving,
title = "Improving Open Relation Extraction via Sentence Re-Structuring",
author = "Schmidek, Jordan and
Barbosa, Denilson",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1038_Paper.pdf",
pages = "3720--3723",
abstract = "Information Extraction is an important task in Natural Language Processing, consisting of finding a structured representation for the information expressed in natural language text. Two key steps in information extraction are identifying the entities mentioned in the text, and the relations among those entities. In the context of Information Extraction for the World Wide Web, unsupervised relation extraction methods, also called Open Relation Extraction (ORE) systems, have become prevalent, due to their effectiveness without domain-specific training data. In general, these systems exploit part-of-speech tags or semantic information from the sentences to determine whether or not a relation exists, and if so, its predicate. This paper discusses some of the issues that arise when even moderately complex sentences are fed into ORE systems. A process for re-structuring such sentences is discussed and evaluated. The proposed approach replaces complex sentences by several others that, together, convey the same meaning and are more amenable to extraction by current ORE systems. The results of an experimental evaluation show that this approach succeeds in reducing the processing time and increasing the accuracy of the state-of-the-art ORE systems.",
}
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%0 Conference Proceedings
%T Improving Open Relation Extraction via Sentence Re-Structuring
%A Schmidek, Jordan
%A Barbosa, Denilson
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F schmidek-barbosa-2014-improving
%X Information Extraction is an important task in Natural Language Processing, consisting of finding a structured representation for the information expressed in natural language text. Two key steps in information extraction are identifying the entities mentioned in the text, and the relations among those entities. In the context of Information Extraction for the World Wide Web, unsupervised relation extraction methods, also called Open Relation Extraction (ORE) systems, have become prevalent, due to their effectiveness without domain-specific training data. In general, these systems exploit part-of-speech tags or semantic information from the sentences to determine whether or not a relation exists, and if so, its predicate. This paper discusses some of the issues that arise when even moderately complex sentences are fed into ORE systems. A process for re-structuring such sentences is discussed and evaluated. The proposed approach replaces complex sentences by several others that, together, convey the same meaning and are more amenable to extraction by current ORE systems. The results of an experimental evaluation show that this approach succeeds in reducing the processing time and increasing the accuracy of the state-of-the-art ORE systems.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/1038_Paper.pdf
%P 3720-3723
Markdown (Informal)
[Improving Open Relation Extraction via Sentence Re-Structuring](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1038_Paper.pdf) (Schmidek & Barbosa, LREC 2014)
ACL