Conference on Empirical Methods in Natural Language Processing (and forerunners) (2014)


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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Alessandro Moschitti | Bo Pang | Walter Daelemans

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Invited Talk: IBM Cognitive Computing - An NLP Renaissance!
Salim Roukos

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Modeling Interestingness with Deep Neural Networks
Jianfeng Gao | Patrick Pantel | Michael Gamon | Xiaodong He | Li Deng

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Translation Modeling with Bidirectional Recurrent Neural Networks
Martin Sundermeyer | Tamer Alkhouli | Joern Wuebker | Hermann Ney

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A Neural Network Approach to Selectional Preference Acquisition
Tim Van de Cruys

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Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics
Douwe Kiela | Léon Bottou

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Identifying Argumentative Discourse Structures in Persuasive Essays
Christian Stab | Iryna Gurevych

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Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System
Zhuoran Wang | Hongliang Chen | Guanchun Wang | Hao Tian | Hua Wu | Haifeng Wang

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A Constituent-Based Approach to Argument Labeling with Joint Inference in Discourse Parsing
Fang Kong | Hwee Tou Ng | Guodong Zhou

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Strongly Incremental Repair Detection
Julian Hough | Matthew Purver

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Semi-Supervised Chinese Word Segmentation Using Partial-Label Learning With Conditional Random Fields
Fan Yang | Paul Vozila

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Accurate Word Segmentation and POS Tagging for Japanese Microblogs: Corpus Annotation and Joint Modeling with Lexical Normalization
Nobuhiro Kaji | Masaru Kitsuregawa

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Revisiting Embedding Features for Simple Semi-supervised Learning
Jiang Guo | Wanxiang Che | Haifeng Wang | Ting Liu

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Combining Punctuation and Disfluency Prediction: An Empirical Study
Xuancong Wang | Khe Chai Sim | Hwee Tou Ng

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Submodularity for Data Selection in Machine Translation
Katrin Kirchhoff | Jeff Bilmes

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Improve Statistical Machine Translation with Context-Sensitive Bilingual Semantic Embedding Model
Haiyang Wu | Daxiang Dong | Xiaoguang Hu | Dianhai Yu | Wei He | Hua Wu | Haifeng Wang | Ting Liu

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Transformation from Discontinuous to Continuous Word Alignment Improves Translation Quality
Zhongjun He | Hua Wu | Haifeng Wang | Ting Liu

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Unsupervised Word Alignment Using Frequency Constraint in Posterior Regularized EM
Hidetaka Kamigaito | Taro Watanabe | Hiroya Takamura | Manabu Okumura

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Asymmetric Features Of Human Generated Translation
Sauleh Eetemadi | Kristina Toutanova

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Syntax-Augmented Machine Translation using Syntax-Label Clustering
Hideya Mino | Taro Watanabe | Eiichiro Sumita

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Testing for Significance of Increased Correlation with Human Judgment
Yvette Graham | Timothy Baldwin

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Syntactic SMT Using a Discriminative Text Generation Model
Yue Zhang | Kai Song | Linfeng Song | Jingbo Zhu | Qun Liu

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Learning Hierarchical Translation Spans
Jingyi Zhang | Masao Utiyama | Eiichiro Sumita | Hai Zhao

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Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation
Rui Wang | Hai Zhao | Bao-Liang Lu | Masao Utiyama | Eiichiro Sumita

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Better Statistical Machine Translation through Linguistic Treatment of Phrasal Verbs
Kostadin Cholakov | Valia Kordoni

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Fitting Sentence Level Translation Evaluation with Many Dense Features
Miloš Stanojević | Khalil Sima’an

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A Human Judgement Corpus and a Metric for Arabic MT Evaluation
Houda Bouamor | Hanan Alshikhabobakr | Behrang Mohit | Kemal Oflazer

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Learning to Differentiate Better from Worse Translations
Francisco Guzmán | Shafiq Joty | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov | Massimo Nicosia

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Two Improvements to Left-to-Right Decoding for Hierarchical Phrase-based Machine Translation
Maryam Siahbani | Anoop Sarkar

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Reordering Model for Forest-to-String Machine Translation
Martin Čmejrek

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Aligning context-based statistical models of language with brain activity during reading
Leila Wehbe | Ashish Vaswani | Kevin Knight | Tom Mitchell

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A Cognitive Model of Semantic Network Learning
Aida Nematzadeh | Afsaneh Fazly | Suzanne Stevenson

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Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can’t See What I Mean
Felix Hill | Anna Korhonen

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Go Climb a Dependency Tree and Correct the Grammatical Errors
Longkai Zhang | Houfeng Wang

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An Unsupervised Model for Instance Level Subcategorization Acquisition
Simon Baker | Roi Reichart | Anna Korhonen

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Parsing low-resource languages using Gibbs sampling for PCFGs with latent annotations
Liang Sun | Jason Mielens | Jason Baldridge

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Incremental Semantic Role Labeling with Tree Adjoining Grammar
Ioannis Konstas | Frank Keller | Vera Demberg | Mirella Lapata

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A Graph-based Approach for Contextual Text Normalization
Cagil Sönmez | Arzucan Özgür

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ReNoun: Fact Extraction for Nominal Attributes
Mohamed Yahya | Steven Whang | Rahul Gupta | Alon Halevy

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Hierarchical Discriminative Classification for Text-Based Geolocation
Benjamin Wing | Jason Baldridge

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Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data
Ivan Vulić | Marie-Francine Moens

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Multi-Predicate Semantic Role Labeling
Haitong Yang | Chengqing Zong

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Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning
Luciano Del Corro | Rainer Gemulla | Gerhard Weikum

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Multi-Resolution Language Grounding with Weak Supervision
R. Koncel-Kedziorski | Hannaneh Hajishirzi | Ali Farhadi

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Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases
Matt Gardner | Partha Talukdar | Jayant Krishnamurthy | Tom Mitchell

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Composition of Word Representations Improves Semantic Role Labelling
Michael Roth | Kristian Woodsend

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Automatic Domain Assignment for Word Sense Alignment
Tommaso Caselli | Carlo Strapparava

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Nothing like Good Old Frequency: Studying Context Filters for Distributional Thesauri
Muntsa Padró | Marco Idiart | Aline Villavicencio | Carlos Ramisch

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Aligning English Strings with Abstract Meaning Representation Graphs
Nima Pourdamghani | Yang Gao | Ulf Hermjakob | Kevin Knight

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A Shortest-path Method for Arc-factored Semantic Role Labeling
Xavier Lluís | Xavier Carreras | Lluís Màrquez

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Semantic Kernels for Semantic Parsing
Iman Saleh | Alessandro Moschitti | Preslav Nakov | Lluís Màrquez | Shafiq Joty

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An I-vector Based Approach to Compact Multi-Granularity Topic Spaces Representation of Textual Documents
Mohamed Morchid | Mohamed Bouallegue | Richard Dufour | Georges Linarès | Driss Matrouf | Renato de Mori

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Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis
Nikolaos Pappas | Andrei Popescu-Belis

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Sentiment Analysis on the People’s Daily
Jiwei Li | Eduard Hovy

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A Joint Segmentation and Classification Framework for Sentiment Analysis
Duyu Tang | Furu Wei | Bing Qin | Li Dong | Ting Liu | Ming Zhou

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Positive Unlabeled Learning for Deceptive Reviews Detection
Yafeng Ren | Donghong Ji | Hongbin Zhang

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Resolving Shell Nouns
Varada Kolhatkar | Graeme Hirst

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A Comparison of Selectional Preference Models for Automatic Verb Classification
Will Roberts | Markus Egg

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Learning to Solve Arithmetic Word Problems with Verb Categorization
Mohammad Javad Hosseini | Hannaneh Hajishirzi | Oren Etzioni | Nate Kushman

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NaturalLI: Natural Logic Inference for Common Sense Reasoning
Gabor Angeli | Christopher D. Manning

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Modeling Term Translation for Document-informed Machine Translation
Fandong Meng | Deyi Xiong | Wenbin Jiang | Qun Liu

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Beyond Parallel Data: Joint Word Alignment and Decipherment Improves Machine Translation
Qing Dou | Ashish Vaswani | Kevin Knight

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Latent Domain Phrase-based Models for Adaptation
Hoang Cuong | Khalil Sima’an

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Translation Rules with Right-Hand Side Lattices
Fabien Cromières | Sadao Kurohashi

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Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval
Ferhan Ture | Elizabeth Boschee

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Semantic-Based Multilingual Document Clustering via Tensor Modeling
Salvatore Romeo | Andrea Tagarelli | Dino Ienco

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Lexical Substitution for the Medical Domain
Martin Riedl | Michael Glass | Alfio Gliozzo

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Question Answering with Subgraph Embeddings
Antoine Bordes | Sumit Chopra | Jason Weston

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Correcting Keyboard Layout Errors and Homoglyphs in Queries
Derek Barnes | Mahesh Joshi | Hassan Sawaf

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Non-linear Mapping for Improved Identification of 1300+ Languages
Ralf Brown

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A Neural Network for Factoid Question Answering over Paragraphs
Mohit Iyyer | Jordan Boyd-Graber | Leonardo Claudino | Richard Socher | Hal Daumé III

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Joint Relational Embeddings for Knowledge-based Question Answering
Min-Chul Yang | Nan Duan | Ming Zhou | Hae-Chang Rim

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Adding High-Precision Links to Wikipedia
Thanapon Noraset | Chandra Bhagavatula | Doug Downey

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Finding Good Enough: A Task-Based Evaluation of Query Biased Summarization for Cross-Language Information Retrieval
Jennifer Williams | Sharon Tam | Wade Shen

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Chinese Poetry Generation with Recurrent Neural Networks
Xingxing Zhang | Mirella Lapata

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Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning
Cody Rioux | Sadid A. Hasan | Yllias Chali

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Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees
Chen Li | Yang Liu | Fei Liu | Lin Zhao | Fuliang Weng

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Analyzing Stemming Approaches for Turkish Multi-Document Summarization
Muhammed Yavuz Nuzumlalı | Arzucan Özgür

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Invited Talk: Learning from Rational Behavior
Thorsten Joachims

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Evaluating Neural Word Representations in Tensor-Based Compositional Settings
Dmitrijs Milajevs | Dimitri Kartsaklis | Mehrnoosh Sadrzadeh | Matthew Purver

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Opinion Mining with Deep Recurrent Neural Networks
Ozan İrsoy | Claire Cardie

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The Inside-Outside Recursive Neural Network model for Dependency Parsing
Phong Le | Willem Zuidema

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A Fast and Accurate Dependency Parser using Neural Networks
Danqi Chen | Christopher Manning

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Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates
Kazi Saidul Hasan | Vincent Ng

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Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers
Chen Chen | Vincent Ng

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Unsupervised Sentence Enhancement for Automatic Summarization
Jackie Chi Kit Cheung | Gerald Penn

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ReferItGame: Referring to Objects in Photographs of Natural Scenes
Sahar Kazemzadeh | Vicente Ordonez | Mark Matten | Tamara Berg

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Unsupervised Template Mining for Semantic Category Understanding
Lei Shi | Shuming Shi | Chin-Yew Lin | Yi-Dong Shen | Yong Rui

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Taxonomy Construction Using Syntactic Contextual Evidence
Anh Tuan Luu | Jung-jae Kim | See Kiong Ng

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Analysing recall loss in named entity slot filling
Glen Pink | Joel Nothman | James R. Curran

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Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features
Deepak Venugopal | Chen Chen | Vibhav Gogate | Vincent Ng

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Syllable weight encodes mostly the same information for English word segmentation as dictionary stress
John K Pate | Mark Johnson

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A Joint Model for Unsupervised Chinese Word Segmentation
Miaohong Chen | Baobao Chang | Wenzhe Pei

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Domain Adaptation for CRF-based Chinese Word Segmentation using Free Annotations
Yijia Liu | Yue Zhang | Wanxiang Che | Ting Liu | Fan Wu

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Balanced Korean Word Spacing with Structural SVM
Changki Lee | Edward Choi | Hyunki Kim

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Morphological Segmentation for Keyword Spotting
Karthik Narasimhan | Damianos Karakos | Richard Schwartz | Stavros Tsakalidis | Regina Barzilay

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What Can We Get From 1000 Tokens? A Case Study of Multilingual POS Tagging For Resource-Poor Languages
Long Duong | Trevor Cohn | Karin Verspoor | Steven Bird | Paul Cook

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An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences
Diego Marcheggiani | Thierry Artières

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Language Modeling with Functional Head Constraint for Code Switching Speech Recognition
Ying Li | Pascale Fung

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A Polynomial-Time Dynamic Oracle for Non-Projective Dependency Parsing
Carlos Gómez-Rodríguez | Francesco Sartorio | Giorgio Satta

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Ambiguity Resolution for Vt-N Structures in Chinese
Yu-Ming Hsieh | Jason S. Chang | Keh-Jiann Chen

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Neural Networks Leverage Corpus-wide Information for Part-of-speech Tagging
Yuta Tsuboi

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System Combination for Grammatical Error Correction
Raymond Hendy Susanto | Peter Phandi | Hwee Tou Ng

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Dependency parsing with latent refinements of part-of-speech tags
Thomas Mueller | Richard Farkas | Alex Judea | Helmut Schmid | Hinrich Schuetze

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Importance weighting and unsupervised domain adaptation of POS taggers: a negative result
Barbara Plank | Anders Johannsen | Anders Søgaard

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POS Tagging of English-Hindi Code-Mixed Social Media Content
Yogarshi Vyas | Spandana Gella | Jatin Sharma | Kalika Bali | Monojit Choudhury

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Data Driven Grammatical Error Detection in Transcripts of Children’s Speech
Eric Morley | Anna Eva Hallin | Brian Roark

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A* CCG Parsing with a Supertag-factored Model
Mike Lewis | Mark Steedman

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A Dependency Parser for Tweets
Lingpeng Kong | Nathan Schneider | Swabha Swayamdipta | Archna Bhatia | Chris Dyer | Noah A. Smith

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Greed is Good if Randomized: New Inference for Dependency Parsing
Yuan Zhang | Tao Lei | Regina Barzilay | Tommi Jaakkola

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A Unified Model for Word Sense Representation and Disambiguation
Xinxiong Chen | Zhiyuan Liu | Maosong Sun

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Reducing Dimensions of Tensors in Type-Driven Distributional Semantics
Tamara Polajnar | Luana Fǎgǎrǎşan | Stephen Clark

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An Etymological Approach to Cross-Language Orthographic Similarity. Application on Romanian
Alina Maria Ciobanu | Liviu P. Dinu

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Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
Arvind Neelakantan | Jeevan Shankar | Alexandre Passos | Andrew McCallum

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Tailor knowledge graph for query understanding: linking intent topics by propagation
Shi Zhao | Yan Zhang

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Queries as a Source of Lexicalized Commonsense Knowledge
Marius Paşca

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Question Answering over Linked Data Using First-order Logic
Shizhu He | Kang Liu | Yuanzhe Zhang | Liheng Xu | Jun Zhao

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Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries
Mandar Joshi | Uma Sawant | Soumen Chakrabarti

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A Regularized Competition Model for Question Difficulty Estimation in Community Question Answering Services
Quan Wang | Jing Liu | Bin Wang | Li Guo

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Vote Prediction on Comments in Social Polls
Isaac Persing | Vincent Ng

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Exploiting Social Relations and Sentiment for Stock Prediction
Jianfeng Si | Arjun Mukherjee | Bing Liu | Sinno Jialin Pan | Qing Li | Huayi Li

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Developing Age and Gender Predictive Lexica over Social Media
Maarten Sap | Gregory Park | Johannes Eichstaedt | Margaret Kern | David Stillwell | Michal Kosinski | Lyle Ungar | Hansen Andrew Schwartz

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Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach
William Yang Wang | Lingpeng Kong | Kathryn Mazaitis | William W. Cohen

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Exploiting Community Emotion for Microblog Event Detection
Gaoyan Ou | Wei Chen | Tengjiao Wang | Zhongyu Wei | Binyang Li | Dongqing Yang | Kam-Fai Wong

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Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure
Kelsey Allen | Giuseppe Carenini | Raymond Ng

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+/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference
Yoonjung Choi | Janyce Wiebe

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A Sentiment-aligned Topic Model for Product Aspect Rating Prediction
Hao Wang | Martin Ester

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Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases
Ashequl Qadir | Ellen Riloff

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Fine-Grained Contextual Predictions for Hard Sentiment Words
Sebastian Ebert | Hinrich Schütze

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An Iterative Link-based Method for Parallel Web Page Mining
Le Liu | Yu Hong | Jun Lu | Jun Lang | Heng Ji | Jianmin Yao

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Human Effort and Machine Learnability in Computer Aided Translation
Spence Green | Sida I. Wang | Jason Chuang | Jeffrey Heer | Sebastian Schuster | Christopher D. Manning

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Exact Decoding for Phrase-Based Statistical Machine Translation
Wilker Aziz | Marc Dymetman | Lucia Specia

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Large-scale Expected BLEU Training of Phrase-based Reordering Models
Michael Auli | Michel Galley | Jianfeng Gao

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Confidence-based Rewriting of Machine Translation Output
Benjamin Marie | Aurélien Max

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Learning Compact Lexicons for CCG Semantic Parsing
Yoav Artzi | Dipanjan Das | Slav Petrov

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Morpho-syntactic Lexical Generalization for CCG Semantic Parsing
Adrienne Wang | Tom Kwiatkowski | Luke Zettlemoyer

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Semantic Parsing Using Content and Context: A Case Study from Requirements Elicitation
Reut Tsarfaty | Ilia Pogrebezky | Guy Weiss | Yaarit Natan | Smadar Szekely | David Harel

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Semantic Parsing with Relaxed Hybrid Trees
Wei Lu

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Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference
David Mimno | Moontae Lee

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Weakly-Supervised Learning with Cost-Augmented Contrastive Estimation
Kevin Gimpel | Mohit Bansal

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Don’t Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation
Alvin Grissom II | He He | Jordan Boyd-Graber | John Morgan | Hal Daumé III

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PCFG Induction for Unsupervised Parsing and Language Modelling
James Scicluna | Colin de la Higuera

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Can characters reveal your native language? A language-independent approach to native language identification
Radu Tudor Ionescu | Marius Popescu | Aoife Cahill

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Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning
Yo Ehara | Yusuke Miyao | Hidekazu Oiwa | Issei Sato | Hiroshi Nakagawa

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Language Transfer Hypotheses with Linear SVM Weights
Shervin Malmasi | Mark Dras

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Predicting Dialect Variation in Immigrant Contexts Using Light Verb Constructions
A. Seza Doğruöz | Preslav Nakov

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Device-Dependent Readability for Improved Text Understanding
A-Yeong Kim | Hyun-Je Song | Seong-Bae Park | Sang-Jo Lee

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Predicting Chinese Abbreviations with Minimum Semantic Unit and Global Constraints
Longkai Zhang | Li Li | Houfeng Wang | Xu Sun

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Using Structured Events to Predict Stock Price Movement: An Empirical Investigation
Xiao Ding | Yue Zhang | Ting Liu | Junwen Duan

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Extracting Clusters of Specialist Terms from Unstructured Text
Aaron Gerow

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Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach
Cornelia Caragea | Florin Adrian Bulgarov | Andreea Godea | Sujatha Das Gollapalli

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Using Mined Coreference Chains as a Resource for a Semantic Task
Heike Adel | Hinrich Schütze

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Financial Keyword Expansion via Continuous Word Vector Representations
Ming-Feng Tsai | Chuan-Ju Wang

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Intrinsic Plagiarism Detection using N-gram Classes
Imene Bensalem | Paolo Rosso | Salim Chikhi

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Verifiably Effective Arabic Dialect Identification
Kareem Darwish | Hassan Sajjad | Hamdy Mubarak

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Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays
Ritwik Banerjee | Song Feng | Jun Seok Kang | Yejin Choi

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Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization
Kuan-Yu Chen | Shih-Hung Liu | Berlin Chen | Ea-Ee Jan | Hsin-Min Wang | Wen-Lian Hsu | Hsin-Hsi Chen

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Staying on Topic: An Indicator of Power in Political Debates
Vinodkumar Prabhakaran | Ashima Arora | Owen Rambow

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Language Modeling with Power Low Rank Ensembles
Ankur P. Parikh | Avneesh Saluja | Chris Dyer | Eric Xing

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Modeling Biological Processes for Reading Comprehension
Jonathan Berant | Vivek Srikumar | Pei-Chun Chen | Abby Vander Linden | Brittany Harding | Brad Huang | Peter Clark | Christopher D. Manning

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Sensicon: An Automatically Constructed Sensorial Lexicon
Serra Sinem Tekiroğlu | Gözde Özbal | Carlo Strapparava

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Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
Jingwei Zhang | Jeremy Salwen | Michael Glass | Alfio Gliozzo

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Glove: Global Vectors for Word Representation
Jeffrey Pennington | Richard Socher | Christopher Manning

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Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures
Kazuma Hashimoto | Pontus Stenetorp | Makoto Miwa | Yoshimasa Tsuruoka

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Combining Distant and Partial Supervision for Relation Extraction
Gabor Angeli | Julie Tibshirani | Jean Wu | Christopher D. Manning

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Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
Kai-Wei Chang | Wen-tau Yih | Bishan Yang | Christopher Meek

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A convex relaxation for weakly supervised relation extraction
Édouard Grave

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Knowledge Graph and Text Jointly Embedding
Zhen Wang | Jianwen Zhang | Jianlin Feng | Zheng Chen

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Abstractive Summarization of Product Reviews Using Discourse Structure
Shima Gerani | Yashar Mehdad | Giuseppe Carenini | Raymond T. Ng | Bita Nejat

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Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency
Li Zhao | Minlie Huang | Haiqiang Chen | Junjun Cheng | Xiaoyan Zhu

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Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach
Yue Hu | Xiaojun Wan

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Fast and Accurate Misspelling Correction in Large Corpora
Octavian Popescu | Ngoc Phuoc An Vo

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Assessing the Impact of Translation Errors on Machine Translation Quality with Mixed-effects Models
Marcello Federico | Matteo Negri | Luisa Bentivogli | Marco Turchi

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Refining Word Segmentation Using a Manually Aligned Corpus for Statistical Machine Translation
Xiaolin Wang | Masao Utiyama | Andrew Finch | Eiichiro Sumita

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Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs
Xiaoning Zhu | Zhongjun He | Hua Wu | Conghui Zhu | Haifeng Wang | Tiejun Zhao

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Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks
Ke M. Tran | Arianna Bisazza | Christof Monz

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Dependency-Based Bilingual Language Models for Reordering in Statistical Machine Translation
Ekaterina Garmash | Christof Monz

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Combining String and Context Similarity for Bilingual Term Alignment from Comparable Corpora
Georgios Kontonatsios | Ioannis Korkontzelos | Jun’ichi Tsujii | Sophia Ananiadou

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Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction
Behrang Q. Zadeh | Siegfried Handschuh

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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Kyunghyun Cho | Bart van Merriënboer | Caglar Gulcehre | Dzmitry Bahdanau | Fethi Bougares | Holger Schwenk | Yoshua Bengio

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Type-based MCMC for Sampling Tree Fragments from Forests
Xiaochang Peng | Daniel Gildea

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Convolutional Neural Networks for Sentence Classification
Yoon Kim

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Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling
Viet-An Nguyen | Jordan Boyd-Graber | Philip Resnik

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Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression
Abdullah Alrajeh | Mahesan Niranjan

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Improved Decipherment of Homophonic Ciphers
Malte Nuhn | Julian Schamper | Hermann Ney

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Cipher Type Detection
Malte Nuhn | Kevin Knight

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Joint Learning of Chinese Words, Terms and Keywords
Ziqiang Cao | Sujian Li | Heng Ji

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Cross-Lingual Part-of-Speech Tagging through Ambiguous Learning
Guillaume Wisniewski | Nicolas Pécheux | Souhir Gahbiche-Braham | François Yvon

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Comparing Representations of Semantic Roles for String-To-Tree Decoding
Marzieh Bazrafshan | Daniel Gildea

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Detecting Non-compositional MWE Components using Wiktionary
Bahar Salehi | Paul Cook | Timothy Baldwin

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Joint Emotion Analysis via Multi-task Gaussian Processes
Daniel Beck | Trevor Cohn | Lucia Specia

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Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics
Zubin Jelveh | Bruce Kogut | Suresh Naidu

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A Model of Individual Differences in Gaze Control During Reading
Niels Landwehr | Sebastian Arzt | Tobias Scheffer | Reinhold Kliegl

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Muli-label Text Categorization with Hidden Components
Li Li | Longkai Zhang | Houfeng Wang

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#TagSpace: Semantic Embeddings from Hashtags
Jason Weston | Sumit Chopra | Keith Adams

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Joint Decoding of Tree Transduction Models for Sentence Compression
Jin-ge Yao | Xiaojun Wan | Jianguo Xiao

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Dependency-based Discourse Parser for Single-Document Summarization
Yasuhisa Yoshida | Jun Suzuki | Tsutomu Hirao | Masaaki Nagata

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Improving Word Alignment using Word Similarity
Theerawat Songyot | David Chiang

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Constructing Information Networks Using One Single Model
Qi Li | Heng Ji | Yu Hong | Sujian Li

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Event Role Extraction using Domain-Relevant Word Representations
Emanuela Boroş | Romaric Besançon | Olivier Ferret | Brigitte Grau

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Modeling Joint Entity and Relation Extraction with Table Representation
Makoto Miwa | Yutaka Sasaki

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ZORE: A Syntax-based System for Chinese Open Relation Extraction
Likun Qiu | Yue Zhang

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Coarse-grained Candidate Generation and Fine-grained Re-ranking for Chinese Abbreviation Prediction
Longkai Zhang | Houfeng Wang | Xu Sun

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Type-Aware Distantly Supervised Relation Extraction with Linked Arguments
Mitchell Koch | John Gilmer | Stephen Soderland | Daniel S. Weld

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Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information
Yuchen Zhang | Nianwen Xue

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Joint Inference for Knowledge Base Population
Liwei Chen | Yansong Feng | Jinghui Mo | Songfang Huang | Dongyan Zhao

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Combining Visual and Textual Features for Information Extraction from Online Flyers
Emilia Apostolova | Noriko Tomuro

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CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection
Derry Tanti Wijaya | Ndapandula Nakashole | Tom M. Mitchell

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Noisy Or-based model for Relation Extraction using Distant Supervision
Ajay Nagesh | Gholamreza Haffari | Ganesh Ramakrishnan

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Search-Aware Tuning for Machine Translation
Lemao Liu | Liang Huang

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Latent-Variable Synchronous CFGs for Hierarchical Translation
Avneesh Saluja | Chris Dyer | Shay B. Cohen

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Gender and Power: How Gender and Gender Environment Affect Manifestations of Power
Vinodkumar Prabhakaran | Emily E. Reid | Owen Rambow

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Online topic model for Twitter considering dynamics of user interests and topic trends
Kentaro Sasaki | Tomohiro Yoshikawa | Takeshi Furuhashi

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Self-disclosure topic model for classifying and analyzing Twitter conversations
JinYeong Bak | Chin-Yew Lin | Alice Oh

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Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts
Jiwei Li | Alan Ritter | Claire Cardie | Eduard Hovy

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Brighter than Gold: Figurative Language in User Generated Comparisons
Vlad Niculae | Cristian Danescu-Niculescu-Mizil

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Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Jing Peng | Anna Feldman | Ekaterina Vylomova

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Learning Spatial Knowledge for Text to 3D Scene Generation
Angel Chang | Manolis Savva | Christopher D. Manning

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A Model of Coherence Based on Distributed Sentence Representation
Jiwei Li | Eduard Hovy

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Discriminative Reranking of Discourse Parses Using Tree Kernels
Shafiq Joty | Alessandro Moschitti

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Recursive Deep Models for Discourse Parsing
Jiwei Li | Rumeng Li | Eduard Hovy

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Recall Error Analysis for Coreference Resolution
Sebastian Martschat | Michael Strube

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A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents
Yufang Hou | Katja Markert | Michael Strube

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Resolving Referring Expressions in Conversational Dialogs for Natural User Interfaces
Asli Celikyilmaz | Zhaleh Feizollahi | Dilek Hakkani-Tur | Ruhi Sarikaya

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Building Chinese Discourse Corpus with Connective-driven Dependency Tree Structure
Yancui Li | Wenhe Feng | Jing Sun | Fang Kong | Guodong Zhou

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Prune-and-Score: Learning for Greedy Coreference Resolution
Chao Ma | Janardhan Rao Doppa | J. Walker Orr | Prashanth Mannem | Xiaoli Fern | Tom Dietterich | Prasad Tadepalli

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Summarizing Online Forum Discussions – Can Dialog Acts of Individual Messages Help?
Sumit Bhatia | Prakhar Biyani | Prasenjit Mitra


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bib (full) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

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Sentiment Analysis of Social Media Texts
Saif M. Mohammad | Xiaodan Zhu

Automatically detecting sentiment of product reviews, blogs, tweets, and SMS messages has attracted extensive interest from both the academia and industry. It has a number of applications, including: tracking sentiment towards products, movies, politicians, etc.; improving customer relation models; detecting happiness and well-being; and improving automatic dialogue systems. In this tutorial, we will describe how you can create a state-of-the-art sentiment analysis system, with a focus on social media posts.We begin with an introduction to sentiment analysis and its various forms: term level, message level, document level, and aspect level. We will describe how sentiment analysis systems are evaluated, especially through recent SemEval shared tasks: Sentiment Analysis of Twitter (SemEval-2013 Task 2, SemEval 2014-Task 9) and Aspect Based Sentiment Analysis (SemEval-2014 Task 4).We will give an overview of the best sentiment analysis systems at this point of time, including those that are conventional statistical systems as well as those using deep learning approaches. We will describe in detail the NRC-Canada systems, which were the overall best performing systems in all three SemEval competitions listed above. These are simple lexical- and sentiment-lexicon features based systems, which are relatively easy to re-implement.We will discuss features that had the most impact (those derived from sentiment lexicons and negation handling). We will present how large tweet-specific sentiment lexicons can be automatically generated and evaluated. We will also show how negation impacts sentiment differently depending on whether the scope of the negation is positive or negative. Finally, we will flesh out limitations of current approaches and promising future directions.

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Spectral Learning Techniques for Weighted Automata, Transducers, and Grammars
Borja Balle | Ariadna Quattoni | Xavier Carreras

In recent years we have seen the development of efficient and provably correct algorithms for learning weighted automata and closely related function classes such as weighted transducers and weighted context-free grammars. The common denominator of all these algorithms is the so-called spectral method, which gives an efficient and robust way to estimate recursively defined functions from empirical estimations of observable statistics. These algorithms are appealing because of the existence of theoretical guarantees (e.g. they are not susceptible to local minima) and because of their efficiency. However, despite their simplicity and wide applicability to real problems, their impact in NLP applications is still moderate. One of the goals of this tutorial is to remedy this situation.The contents that will be presented in this tutorial will offer a complementary perspective with respect to previous tutorials on spectral methods presented at ICML-2012, ICML-2013 and NAACL-2013. Rather than using the language of graphical models and signal processing, we tell the story from the perspective of formal languages and automata theory (without assuming a background in formal algebraic methods). Our presentation highlights the common intuitions lying behind different spectral algorithms by presenting them in a unified framework based on the concepts of low-rank factorizations and completions of Hankel matrices. In addition, we provide an interpretation of the method in terms of forward and backward recursions for automata and grammars. This provides extra intuitions about the method and stresses the importance of matrix factorization for learning automata and grammars. We believe that this complementary perspective might be appealing for an NLP audience and serve to put spectral learning in a wider and, perhaps for some, more familiar context. Our hope is that this will broaden the understanding of these methods by the NLP community and empower many researchers to apply these techniques to novel problems.The content of the tutorial will be divided into four blocks of 45 minutes each, as follows. The first block will introduce the basic definitions of weighted automata and Hankel matrices, and present a key connection between the fundamental theorem of weighted automata and learning. In the second block we will discuss the case of probabilistic automata in detail, touching upon all aspects from the underlying theory to the tricks required to achieve accurate and scalable learning algorithms. The third block will present extensions to related models, including sequence tagging models, finite-state transducers and weighted context-free grammars. The last block will describe a general framework for using spectral techniques in more general situations where a matrix completion pre-processing step is required; several applications of this approach will be described.

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Semantic Parsing with Combinatory Categorial Grammars
Yoav Artzi | Nicholas Fitzgerald | Luke Zettlemoyer

Semantic parsers map natural language sentences to formal representations of their underlying meaning. Building accurate semantic parsers without prohibitive engineering costs is a long-standing, open research problem.The tutorial will describe general principles for building semantic parsers. The presentation will be divided into two main parts: learning and modeling. In the learning part, we will describe a unified approach for learning Combinatory Categorial Grammar (CCG) semantic parsers, that induces both a CCG lexicon and the parameters of a parsing model. The approach learns from data with labeled meaning representations, as well as from more easily gathered weak supervision. It also enables grounded learning where the semantic parser is used in an interactive environment, for example to read and execute instructions. The modeling section will include best practices for grammar design and choice of semantic representation. We will motivate our use of lambda calculus as a language for building and representing meaning with examples from several domains.The ideas we will discuss are widely applicable. The semantic modeling approach, while implemented in lambda calculus, could be applied to many other formal languages. Similarly, the algorithms for inducing CCG focus on tasks that are formalism independent, learning the meaning of words and estimating parsing parameters. No prior knowledge of CCG is required. The tutorial will be backed by implementation and experiments in the University of Washington Semantic Parsing Framework (UW SPF, http://yoavartzi.com/spf).

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Linear Programming Decoders in Natural Language Processing: From Integer Programming to Message Passing and Dual Decomposition
André F. T. Martins

This tutorial will cover the theory and practice of linear programming decoders. This class of decoders encompasses a variety of techniques that have enjoyed great success in devising structured models for natural language processing (NLP). Along the tutorial, we provide a unified view of different algorithms and modeling techniques, including belief propagation, dual decomposition, integer linear programming, Markov logic, and constrained conditional models. Various applications in NLP will serve as a motivation.There is a long string of work using integer linear programming (ILP) formulations in NLP, for example in semantic role labeling, machine translation, summarization, dependency parsing, coreference resolution, and opinion mining, to name just a few. At the heart of these approaches is the ability to encode logic and budget constraints (common in NLP and information retrieval) as linear inequalities. Thanks to general purpose solvers (such as Gurobi, CPLEX, or GLPK), the practitioner can abstract away from the decoding algorithm and focus on developing a powerful model. A disadvantage, however, is that general solvers do not scale well to large problem instances, since they fail to exploit the structure of the problem.This is where graphical models come into play. In this tutorial, we show that most logic and budget constraints that arise in NLP can be cast in this framework. This opens the door for the use of message-passing algorithms, such as belief propagation and variants thereof. An alternative are algorithms based on dual decomposition, such as the subgradient method or AD3. These algorithms have achieved great success in a variety of applications, such as parsing, corpus-wide tagging, machine translation, summarization, joint coreference resolution and quotation attribution, and semantic role labeling. Interestingly, most decoders used in these works can be regarded as structure-aware solvers for addressing relaxations of integer linear programs. All these algorithms have a similar consensus-based architecture: they repeatedly perform certain "local" operations in the graph, until some form of local agreement is achieved. The local operations are performed at each factor, and they range between computing marginals, max-marginals, an optimal configuration, or a small quadratic problem, all of which are commonly tractable and efficient in a wide range of problems.As a companion of this tutorial, we provide an open-source implementation of some of the algorithms described above, available at http://www.ark.cs.cmu.edu/AD3.

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Syntax-Based Statistical Machine Translation
Philip Williams | Philipp Koehn

The tutorial explains in detail syntax-based statistical machine translation with synchronous context free grammars (SCFG). It is aimed at researchers who have little background in this area, and gives a comprehensive overview about the main models and methods.While syntax-based models in statistical machine translation have a long history, spanning back almost 20 years, they have only recently shown superior translation quality over the more commonly used phrase-based models, and are now considered state of the art for some language pairs, such as Chinese-English (since ISI's submission to NIST 2006), and English-German (since Edinburgh's submission to WMT 2012).While the field is very dynamic, there is a core set of methods that have become dominant. Such SCFG models are implemented in the open source machine translation toolkit Moses, and the tutors draw from the practical experience of its development.The tutorial focuses on explaining core established concepts in SCFG-based approaches, which are the most popular in this area. The main goal of the tutorial is for the audience to understand how these systems work end-to-end. We review as much relevant literature as necessary, but the tutorial is not a primarily research survey.The tutorial is rounded up with open problems and advanced topics, such as computational challenges, different formalisms for syntax-based models and inclusion of semantics.

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Embedding Methods for Natural Language Processing
Antoine Bordes | Jason Weston

Embedding-based models are popular tools in Natural Language Processing these days. In this tutorial, our goal is to provide an overview of the main advances in this domain. These methods learn latent representations of words, as well as database entries that can then be used to do semantic search, automatic knowledge base construction, natural language understanding, etc. Our current plan is to split the tutorial into 2 sessions of 90 minutes, with a 30 minutes coffee break in the middle, so that we can cover in a first session the basics of learning embeddings and advanced models in the second session. This is detailed in the following.Part 1: Unsupervised and Supervised EmbeddingsWe introduce models that embed tokens (words, database entries) by representing them as low dimensional embedding vectors. Unsupervised and supervised methods will be discussed, including SVD, Word2Vec, Paragraph Vectors, SSI, Wsabie and others. A comparison between methods will be made in terms of applicability, type of loss function (ranking loss, reconstruction loss, classification loss), regularization, etc. The use of these models in several NLP tasks will be discussed, including question answering, frame identification, knowledge extraction and document retrieval.Part 2: Embeddings for Multi-relational DataThis second part will focus mostly on the construction of embeddings for multi-relational data, that is when tokens can be interconnected in different ways in the data such as in knowledge bases for instance. Several methods based on tensor factorization, collective matrix factorization, stochastic block models or energy-based learning will be presented. The task of link prediction in a knowledge base will be used as an application example. Multiple empirical results on the use of embedding models to align textual information to knowledge bases will also be presented, together with some demos if time permits.

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Natural Language Processing of Arabic and its Dialects
Mona Diab | Nizar Habash

This tutorial introduces the different challenges and current solutions to the automatic processing of Arabic and its dialects. The tutorial has two parts: First, we present a discussion of generic issues relevant to Arabic NLP and detail dialectal linguistic issues and the challenges they pose for NLP. In the second part, we review the state-of-the-art in Arabic processing covering several enabling technologies and applications, e.g., dialect identification, morphological processing (analysis, disambiguation, tokenization, POS tagging), parsing, and machine translation.

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Text Quantification
Fabrizio Sebastiani

In recent years it has been pointed out that, in a number of applications involving (text) classification, the final goal is not determining which class (or classes) individual unlabelled data items belong to, but determining the prevalence (or "relative frequency") of each class in the unlabelled data. The latter task is known as quantification. Assume a market research agency runs a poll in which they ask the question "What do you think of the recent ad campaign for product X?" Once the poll is complete, they may want to classify the resulting textual answers according to whether they belong or not to the class LovedTheCampaign. The agency is likely not interested in whether a specific individual belongs to the class LovedTheCampaign, but in knowing how many respondents belong to it, i.e., in knowing the prevalence of the class. In other words, the agency is interested not in classification, but in quantification. Essentially, quantification is classification tackled at the aggregate (rather than at the individual) level. The research community has recently shown a growing interest in tackling quantification as a task in its own right. One of the reasons is that, since the goal of quantification is different than that of classification, quantification requires evaluation measures different than for classification. A second, related reason is that using a method optimized for classification accuracy is suboptimal when quantification accuracy is the real goal. A third reason is the growing awareness that quantification is going to be more and more important; with the advent of big data, more and more application contexts are going to spring up in which we will simply be happy with analyzing data at the aggregate (rather than at the individual) level. The goal of this tutorial is to introduce the audience to the problem of quantification, to the techniques that have been proposed for solving it, to the metrics used to evaluate them, and to the problems that are still open in the area.