Conference on Empirical Methods in Natural Language Processing (2014)


up

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

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

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

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

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

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

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

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

pdf bib
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

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

pdf bib
Strongly Incremental Repair Detection
Julian Hough | Matthew Purver

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

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

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

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

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

pdf bib
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

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

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

pdf bib
Asymmetric Features Of Human Generated Translation
Sauleh Eetemadi | Kristina Toutanova

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pdf bib
Multi-Predicate Semantic Role Labeling
Haitong Yang | Chengqing Zong

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

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

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

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

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

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

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

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

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

pdf bib
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

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

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

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

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

pdf bib
Resolving Shell Nouns
Varada Kolhatkar | Graeme Hirst

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pdf bib
Invited Talk: Learning from Rational Behavior
Thorsten Joachims

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pdf bib
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pdf bib
Queries as a Source of Lexicalized Commonsense Knowledge
Marius Paşca

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

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

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

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

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

pdf bib
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pdf bib
Semantic Parsing with Relaxed Hybrid Trees
Wei Lu

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

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

pdf bib
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

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

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

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

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

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

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

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

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

pdf bib
Extracting Clusters of Specialist Terms from Unstructured Text
Aaron Gerow

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

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

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

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

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

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

pdf bib
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

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

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

pdf bib
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

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

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

pdf bib
GloVe: Global Vectors for Word Representation
Jeffrey Pennington | Richard Socher | Christopher Manning

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

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

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

pdf bib
A convex relaxation for weakly supervised relation extraction
Édouard Grave

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

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

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

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

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

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

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

pdf bib
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

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

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

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

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

pdf bib
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

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

pdf bib
Convolutional Neural Networks for Sentence Classification
Yoon Kim

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

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

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

pdf bib
Cipher Type Detection
Malte Nuhn | Kevin Knight

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

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

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

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

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

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

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

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

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

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

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

pdf bib
Improving Word Alignment using Word Similarity
Theerawat Songyot | David Chiang

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pdf bib
Recall Error Analysis for Coreference Resolution
Sebastian Martschat | Michael Strube

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

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

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

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

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


up

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

bib
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.

bib
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.

bib
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).

bib
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.

bib
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.

bib
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.

bib
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.

bib
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.