Conference on Empirical Methods in Natural Language Processing (and forerunners) (2015)


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Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Lluís Màrquez | Chris Callison-Burch | Jian Su

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Language Understanding for Text-based Games using Deep Reinforcement Learning
Karthik Narasimhan | Tejas Kulkarni | Regina Barzilay

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Distributional vectors encode referential attributes
Abhijeet Gupta | Gemma Boleda | Marco Baroni | Sebastian Padó

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Building a shared world: mapping distributional to model-theoretic semantic spaces
Aurélie Herbelot | Eva Maria Vecchi

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Dependency Graph-to-String Translation
Liangyou Li | Andy Way | Qun Liu

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Reordering Grammar Induction
Miloš Stanojević | Khalil Sima’an

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Syntax-based Rewriting for Simultaneous Machine Translation
He He | Alvin Grissom II | John Morgan | Jordan Boyd-Graber | Hal Daumé III

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Identifying Political Sentiment between Nation States with Social Media
Nathanael Chambers | Victor Bowen | Ethan Genco | Xisen Tian | Eric Young | Ganesh Harihara | Eugene Yang

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Open Extraction of Fine-Grained Political Statements
David Bamman | Noah A. Smith

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Using Personal Traits For Brand Preference Prediction
Chao Yang | Shimei Pan | Jalal Mahmud | Huahai Yang | Padmini Srinivasan

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Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
Tuan Tran | Nam Khanh Tran | Asmelash Teka Hadgu | Robert Jäschke

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System Combination for Multi-document Summarization
Kai Hong | Mitchell Marcus | Ani Nenkova

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Phrase-based Compressive Cross-Language Summarization
Jin-ge Yao | Xiaojun Wan | Jianguo Xiao

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Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE
Yvette Graham

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Indicative Tweet Generation: An Extractive Summarization Problem?
Priya Sidhaye | Jackie Chi Kit Cheung

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Visual Bilingual Lexicon Induction with Transferred ConvNet Features
Douwe Kiela | Ivan Vulić | Stephen Clark

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Cross Lingual Sentiment Analysis using Modified BRAE
Sarthak Jain | Shashank Batra

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Monotone Submodularity in Opinion Summaries
Jayanth Jayanth | Jayaprakash Sundararaj | Pushpak Bhattacharyya

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Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models
Lingjia Deng | Janyce Wiebe

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Learning to Recognize Affective Polarity in Similes
Ashequl Qadir | Ellen Riloff | Marilyn Walker

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Cross-document Event Coreference Resolution based on Cross-media Features
Tongtao Zhang | Hongzhi Li | Heng Ji | Shih-Fu Chang

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A Survey of Current Datasets for Vision and Language Research
Francis Ferraro | Nasrin Mostafazadeh | Ting-Hao Huang | Lucy Vanderwende | Jacob Devlin | Michel Galley | Margaret Mitchell

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Combining Geometric, Textual and Visual Features for Predicting Prepositions in Image Descriptions
Arnau Ramisa | Josiah Wang | Ying Lu | Emmanuel Dellandrea | Francesc Moreno-Noguer | Robert Gaizauskas

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On A Strictly Convex IBM Model 1
Andrei Simion | Michael Collins | Cliff Stein

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Factorization of Latent Variables in Distributional Semantic Models
Arvid Österlund | David Ödling | Magnus Sahlgren

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Non-lexical neural architecture for fine-grained POS Tagging
Matthieu Labeau | Kevin Löser | Alexandre Allauzen

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Online Representation Learning in Recurrent Neural Language Models
Marek Rei

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A Model of Zero-Shot Learning of Spoken Language Understanding
Majid Yazdani | James Henderson

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Modeling Tweet Arrival Times using Log-Gaussian Cox Processes
Michal Lukasik | P. K. Srijith | Trevor Cohn | Kalina Bontcheva

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Pre-Computable Multi-Layer Neural Network Language Models
Jacob Devlin | Chris Quirk | Arul Menezes

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Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik

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Aligning Knowledge and Text Embeddings by Entity Descriptions
Huaping Zhong | Jianwen Zhang | Zhen Wang | Hai Wan | Zheng Chen

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An Empirical Analysis of Optimization for Max-Margin NLP
Jonathan K. Kummerfeld | Taylor Berg-Kirkpatrick | Dan Klein

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Learning Better Embeddings for Rare Words Using Distributional Representations
Irina Sergienya | Hinrich Schütze

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Composing Relationships with Translations
Alberto García-Durán | Antoine Bordes | Nicolas Usunier

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Noise or additional information? Leveraging crowdsource annotation item agreement for natural language tasks.
Emily Jamison | Iryna Gurevych

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Evaluation methods for unsupervised word embeddings
Tobias Schnabel | Igor Labutov | David Mimno | Thorsten Joachims

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Efficient Methods for Incorporating Knowledge into Topic Models
Yi Yang | Doug Downey | Jordan Boyd-Graber

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Traversing Knowledge Graphs in Vector Space
Kelvin Guu | John Miller | Percy Liang

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Density-Driven Cross-Lingual Transfer of Dependency Parsers
Mohammad Sadegh Rasooli | Michael Collins

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A Neural Network Model for Low-Resource Universal Dependency Parsing
Long Duong | Trevor Cohn | Steven Bird | Paul Cook

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Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs
Miguel Ballesteros | Chris Dyer | Noah A. Smith

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Sentence Compression by Deletion with LSTMs
Katja Filippova | Enrique Alfonseca | Carlos A. Colmenares | Lukasz Kaiser | Oriol Vinyals

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An Empirical Comparison Between N-gram and Syntactic Language Models for Word Ordering
Jiangming Liu | Yue Zhang

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A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush | Sumit Chopra | Jason Weston

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Scientific Article Summarization Using Citation-Context and Article’s Discourse Structure
Arman Cohan | Nazli Goharian

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Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags
Yeyun Gong | Qi Zhang | Xuanjing Huang

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A Graph-based Readability Assessment Method using Word Coupling
Zhiwei Jiang | Gang Sun | Qing Gu | Tao Bai | Daoxu Chen

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More Features Are Not Always Better: Evaluating Generalizing Models in Incident Type Classification of Tweets
Axel Schulz | Christian Guckelsberger | Benedikt Schmidt

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Flexible Domain Adaptation for Automated Essay Scoring Using Correlated Linear Regression
Peter Phandi | Kian Ming A. Chai | Hwee Tou Ng

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Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection
Ruty Rinott | Lena Dankin | Carlos Alzate Perez | Mitesh M. Khapra | Ehud Aharoni | Noam Slonim

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Spelling Correction of User Search Queries through Statistical Machine Translation
Saša Hasan | Carmen Heger | Saab Mansour

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Human Evaluation of Grammatical Error Correction Systems
Roman Grundkiewicz | Marcin Junczys-Dowmunt | Edward Gillian

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Learning a Deep Hybrid Model for Semi-Supervised Text Classification
Alexander Ororbia II | C. Lee Giles | David Reitter

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Joint Embedding of Query and Ad by Leveraging Implicit Feedback
Sungjin Lee | Yifan Hu

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Automatic Extraction of Time Expressions Accross Domains in French Narratives
Mike Donald Tapi Nzali | Xavier Tannier | Aurélie Névéol

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Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics
David S. Batista | Bruno Martins | Mário J. Silva

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Extraction and generalisation of variables from scientific publications
Erwin Marsi | Pinar Öztürk

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Named entity recognition with document-specific KB tag gazetteers
Will Radford | Xavier Carreras | James Henderson

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“A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce”: Learning State Changing Verbs from Wikipedia Revision History
Derry Tanti Wijaya | Ndapandula Nakashole | Tom Mitchell

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Improving Distant Supervision for Information Extraction Using Label Propagation Through Lists
Lidong Bing | Sneha Chaudhari | Richard Wang | William Cohen

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An Entity-centric Approach for Overcoming Knowledge Graph Sparsity
Manjunath Hegde | Partha P. Talukdar

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Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Kun Xu | Yansong Feng | Songfang Huang | Dongyan Zhao

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A Baseline Temporal Tagger for all Languages
Jannik Strötgen | Michael Gertz

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Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings
Nanyun Peng | Mark Dredze

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Inferring Binary Relation Schemas for Open Information Extraction
Kangqi Luo | Xusheng Luo | Kenny Zhu

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LDTM: A Latent Document Type Model for Cumulative Citation Recommendation
Jingang Wang | Dandan Song | Zhiwei Zhang | Lejian Liao | Luo Si | Chin-Yew Lin

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Online Sentence Novelty Scoring for Topical Document Streams
Sungjin Lee

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Global Thread-level Inference for Comment Classification in Community Question Answering
Shafiq Joty | Alberto Barrón-Cedeño | Giovanni Da San Martino | Simone Filice | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov

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Key Concept Identification for Medical Information Retrieval
Jiaping Zheng | Hong Yu

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Image-Mediated Learning for Zero-Shot Cross-Lingual Document Retrieval
Ruka Funaki | Hideki Nakayama

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Detecting Risks in the Banking System by Sentiment Analysis
Clemens Nopp | Allan Hanbury

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Sentiment Flow - A General Model of Web Review Argumentation
Henning Wachsmuth | Johannes Kiesel | Benno Stein

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Neural Networks for Open Domain Targeted Sentiment
Meishan Zhang | Yue Zhang | Duy-Tin Vo

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Extracting Condition-Opinion Relations Toward Fine-grained Opinion Mining
Yuki Nakayama | Atsushi Fujii

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A large annotated corpus for learning natural language inference
Samuel R. Bowman | Gabor Angeli | Christopher Potts | Christopher D. Manning

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Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language
Luheng He | Mike Lewis | Luke Zettlemoyer

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Name List Only? Target Entity Disambiguation in Short Texts
Yixin Cao | Juanzi Li | Xiaofei Guo | Shuanhu Bai | Heng Ji | Jie Tang

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Biography-Dependent Collaborative Entity Archiving for Slot Filling
Yu Hong | Xiaobin Wang | Yadong Chen | Jian Wang | Tongtao Zhang | Heng Ji

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Stochastic Top-k ListNet
Tianyi Luo | Dong Wang | Rong Liu | Yiqiao Pan

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Exploring Markov Logic Networks for Question Answering
Tushar Khot | Niranjan Balasubramanian | Eric Gribkoff | Ashish Sabharwal | Peter Clark | Oren Etzioni

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Language and Domain Independent Entity Linking with Quantified Collective Validation
Han Wang | Jin Guang Zheng | Xiaogang Ma | Peter Fox | Heng Ji

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Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin | Zhiyuan Liu | Huanbo Luan | Maosong Sun | Siwei Rao | Song Liu

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Corpus-level Fine-grained Entity Typing Using Contextual Information
Yadollah Yaghoobzadeh | Hinrich Schütze

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Knowledge Base Unification via Sense Embeddings and Disambiguation
Claudio Delli Bovi | Luis Espinosa-Anke | Roberto Navigli

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Open-Domain Name Error Detection using a Multi-Task RNN
Hao Cheng | Hao Fang | Mari Ostendorf

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Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning
Isabelle Augenstein | Andreas Vlachos | Diana Maynard

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Sieve-Based Spatial Relation Extraction with Expanding Parse Trees
Jennifer D’Souza | Vincent Ng

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Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On The Difficulty of Detecting Characters in Literary Texts
Hardik Vala | David Jurgens | Andrew Piper | Derek Ruths

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Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization
Jonghoon Kim | François Rousseau | Michalis Vazirgiannis

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Predicting the Structure of Cooking Recipes
Jermsak Jermsurawong | Nizar Habash

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TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text Clustering
Linmei Hu | Juanzi Li | Xiaoli Li | Chao Shao | Xuzhong Wang

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Sentence Modeling with Gated Recursive Neural Network
Xinchi Chen | Xipeng Qiu | Chenxi Zhu | Shiyu Wu | Xuanjing Huang

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Learning Timeline Difference for Text Categorization
Fumiyo Fukumoto | Yoshimi Suzuki

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Summarizing Topical Contents from PubMed Documents Using a Thematic Analysis
Sun Kim | Lana Yeganova | W. John Wilbur

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Recognizing Biographical Sections in Wikipedia
Alessio Palmero Aprosio | Sara Tonelli

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Learn to Solve Algebra Word Problems Using Quadratic Programming
Lipu Zhou | Shuaixiang Dai | Liwei Chen

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An Unsupervised Method for Discovering Lexical Variations in Roman Urdu Informal Text
Abdul Rafae | Abdul Qayyum | Muhammad Moeenuddin | Asim Karim | Hassan Sajjad | Faisal Kamiran

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Component-Enhanced Chinese Character Embeddings
Yanran Li | Wenjie Li | Fei Sun | Sujian Li

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Multi-label Text Categorization with Joint Learning Predictions-as-Features Method
Li Li | Houfeng Wang | Xu Sun | Baobao Chang | Shi Zhao | Lei Sha

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A Framework for Comparing Groups of Documents
Arun Maiya

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C3EL: A Joint Model for Cross-Document Co-Reference Resolution and Entity Linking
Sourav Dutta | Gerhard Weikum

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Joint Mention Extraction and Classification with Mention Hypergraphs
Wei Lu | Dan Roth

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FINET: Context-Aware Fine-Grained Named Entity Typing
Luciano Del Corro | Abdalghani Abujabal | Rainer Gemulla | Gerhard Weikum

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Joint Entity Recognition and Disambiguation
Gang Luo | Xiaojiang Huang | Chin-Yew Lin | Zaiqing Nie

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How Much Information Does a Human Translator Add to the Original?
Barret Zoph | Marjan Ghazvininejad | Kevin Knight

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Hierarchical Recurrent Neural Network for Document Modeling
Rui Lin | Shujie Liu | Muyun Yang | Mu Li | Ming Zhou | Sheng Li

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Auto-Sizing Neural Networks: With Applications to n-gram Language Models
Kenton Murray | David Chiang

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Dual Decomposition Inference for Graphical Models over Strings
Nanyun Peng | Ryan Cotterell | Jason Eisner

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Discourse parsing for multi-party chat dialogues
Stergos Afantenos | Eric Kow | Nicholas Asher | Jérémy Perret

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Joint prediction in MST-style discourse parsing for argumentation mining
Andreas Peldszus | Manfred Stede

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Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner

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Semantic Role Labeling with Neural Network Factors
Nicholas FitzGerald | Oscar Täckström | Kuzman Ganchev | Dipanjan Das

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RELLY: Inferring Hypernym Relationships Between Relational Phrases
Adam Grycner | Gerhard Weikum | Jay Pujara | James Foulds | Lise Getoor

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Mise en Place: Unsupervised Interpretation of Instructional Recipes
Chloé Kiddon | Ganesa Thandavam Ponnuraj | Luke Zettlemoyer | Yejin Choi

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Semantic Framework for Comparison Structures in Natural Language
Omid Bakhshandeh | James Allen

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Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words
Debanjan Ghosh | Weiwei Guo | Smaranda Muresan

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Incorporating Trustiness and Collective Synonym/Contrastive Evidence into Taxonomy Construction
Anh Tuan Luu | Jung-jae Kim | See Kiong Ng

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Learning to Automatically Solve Logic Grid Puzzles
Arindam Mitra | Chitta Baral

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Improving fast_align by Reordering
Chenchen Ding | Masao Utiyama | Eiichiro Sumita

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Touch-Based Pre-Post-Editing of Machine Translation Output
Benjamin Marie | Aurélien Max

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A Discriminative Training Procedure for Continuous Translation Models
Quoc-Khanh Do | Alexandre Allauzen | François Yvon

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System Combination for Machine Translation through Paraphrasing
Wei-Yun Ma | Kathleen McKeown

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Hierarchical Incremental Adaptation for Statistical Machine Translation
Joern Wuebker | Spence Green | John DeNero

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ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural Networks
Rohit Gupta | Constantin Orăsan | Josef van Genabith

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Investigating Continuous Space Language Models for Machine Translation Quality Estimation
Kashif Shah | Raymond W. M. Ng | Fethi Bougares | Lucia Specia

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Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages
Tomer Levinboim | David Chiang

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Translation Invariant Word Embeddings
Kejun Huang | Matt Gardner | Evangelos Papalexakis | Christos Faloutsos | Nikos Sidiropoulos | Tom Mitchell | Partha P. Talukdar | Xiao Fu

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Hierarchical Phrase-based Stream Decoding
Andrew Finch | Xiaolin Wang | Masao Utiyama | Eiichiro Sumita

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Rule Selection with Soft Syntactic Features for String-to-Tree Statistical Machine Translation
Fabienne Braune | Nina Seemann | Alexander Fraser

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Motivating Personality-aware Machine Translation
Shachar Mirkin | Scott Nowson | Caroline Brun | Julien Perez

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Trans-gram, Fast Cross-lingual Word-embeddings
Jocelyn Coulmance | Jean-Marc Marty | Guillaume Wenzek | Amine Benhalloum

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The Overall Markedness of Discourse Relations
Lifeng Jin | Marie-Catherine de Marneffe

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Experiments in Open Domain Deception Detection
Verónica Pérez-Rosas | Rada Mihalcea

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A model of rapid phonotactic generalization
Tal Linzen | Timothy O’Donnell

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Automatically Solving Number Word Problems by Semantic Parsing and Reasoning
Shuming Shi | Yuehui Wang | Chin-Yew Lin | Xiaojiang Liu | Yong Rui

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Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation
Michael Pust | Ulf Hermjakob | Kevin Knight | Daniel Marcu | Jonathan May

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The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization
Phong Le | Willem Zuidema

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Alignment-Based Compositional Semantics for Instruction Following
Jacob Andreas | Dan Klein

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Do we need bigram alignment models? On the effect of alignment quality on transduction accuracy in G2P
Steffen Eger

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Keyboard Logs as Natural Annotations for Word Segmentation
Fumihiko Takahasi | Shinsuke Mori

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Long Short-Term Memory Neural Networks for Chinese Word Segmentation
Xinchi Chen | Xipeng Qiu | Chenxi Zhu | Pengfei Liu | Xuanjing Huang

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Semi-supervised Chinese Word Segmentation based on Bilingual Information
Wei Chen | Bo Xu

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Hierarchical Back-off Modeling of Hiero Grammar based on Non-parametric Bayesian Model
Hidetaka Kamigaito | Taro Watanabe | Hiroya Takamura | Manabu Okumura | Eiichiro Sumita

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Consistency-Aware Search for Word Alignment
Shiqi Shen | Yang Liu | Maosong Sun | Huanbo Luan

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Graph-Based Collective Lexical Selection for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Shujian Huang | Xianpei Han | Junfeng Yao

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Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Biao Zhang | Yang Liu | Junfeng Yao | Min Zhang

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How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models
Shafiq Joty | Hassan Sajjad | Nadir Durrani | Kamla Al-Mannai | Ahmed Abdelali | Stephan Vogel

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Detecting Content-Heavy Sentences: A Cross-Language Case Study
Junyi Jessy Li | Ani Nenkova

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Search-Aware Tuning for Hierarchical Phrase-based Decoding
Feifei Zhai | Liang Huang | Kai Zhao

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Part-of-speech Taggers for Low-resource Languages using CCA Features
Young-Bum Kim | Benjamin Snyder | Ruhi Sarikaya

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An Improved Tag Dictionary for Faster Part-of-Speech Tagging
Robert Moore

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Improving Arabic Diacritization through Syntactic Analysis
Anas Shahrour | Salam Khalifa | Nizar Habash

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Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing
Meishan Zhang | Yue Zhang

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Efficient Inner-to-outer Greedy Algorithm for Higher-order Labeled Dependency Parsing
Xuezhe Ma | Eduard Hovy

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Online Updating of Word Representations for Part-of-Speech Tagging
Wenpeng Yin | Tobias Schnabel | Hinrich Schütze

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Empty Category Detection using Path Features and Distributed Case Frames
Shunsuke Takeno | Masaaki Nagata | Kazuhide Yamamoto

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Foreebank: Syntactic Analysis of Customer Support Forums
Rasoul Kaljahi | Jennifer Foster | Johann Roturier | Corentin Ribeyre | Teresa Lynn | Joseph Le Roux

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Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data
Eliyahu Kiperwasser | Yoav Goldberg

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Improved Transition-Based Parsing and Tagging with Neural Networks
Chris Alberti | David Weiss | Greg Coppola | Slav Petrov

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Syntactic Parse Fusion
Do Kook Choe | David McClosky | Eugene Charniak

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Not All Contexts Are Created Equal: Better Word Representations with Variable Attention
Wang Ling | Yulia Tsvetkov | Silvio Amir | Ramón Fermandez | Chris Dyer | Alan W Black | Isabel Trancoso | Chu-Cheng Lin

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An Improved Non-monotonic Transition System for Dependency Parsing
Matthew Honnibal | Mark Johnson

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Improving Statistical Machine Translation with a Multilingual Paraphrase Database
Ramtin Mehdizadeh Seraj | Maryam Siahbani | Anoop Sarkar

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Learning Semantic Representations for Nonterminals in Hierarchical Phrase-Based Translation
Xing Wang | Deyi Xiong | Min Zhang

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A Comparison between Count and Neural Network Models Based on Joint Translation and Reordering Sequences
Andreas Guta | Tamer Alkhouli | Jan-Thorsten Peter | Joern Wuebker | Hermann Ney

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Effective Approaches to Attention-based Neural Machine Translation
Thang Luong | Hieu Pham | Christopher D. Manning

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Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
Duyu Tang | Bing Qin | Ting Liu

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Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings
Pengfei Liu | Shafiq Joty | Helen Meng

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Joint A* CCG Parsing and Semantic Role Labelling
Mike Lewis | Luheng He | Luke Zettlemoyer

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Improving Semantic Parsing with Enriched Synchronous Context-Free Grammar
Junhui Li | Muhua Zhu | Wei Lu | Guodong Zhou

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Solving Geometry Problems: Combining Text and Diagram Interpretation
Minjoon Seo | Hannaneh Hajishirzi | Ali Farhadi | Oren Etzioni | Clint Malcolm

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Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Yevgeni Berzak | Andrei Barbu | Daniel Harari | Boris Katz | Shimon Ullman

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Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction
Matt Gardner | Tom Mitchell

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Representing Text for Joint Embedding of Text and Knowledge Bases
Kristina Toutanova | Danqi Chen | Patrick Pantel | Hoifung Poon | Pallavi Choudhury | Michael Gamon

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A Utility Model of Authors in the Scientific Community
Yanchuan Sim | Bryan Routledge | Noah A. Smith

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Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Wang Ling | Chris Dyer | Alan W Black | Isabel Trancoso | Ramón Fermandez | Silvio Amir | Luís Marujo | Tiago Luís

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Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Jianpeng Cheng | Dimitri Kartsaklis

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Conversation Trees: A Grammar Model for Topic Structure in Forums
Annie Louis | Shay B. Cohen

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Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
Jingwei Zhang | Aaron Gerow | Jaan Altosaar | James Evans | Richard Jean So

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Molding CNNs for text: non-linear, non-consecutive convolutions
Tao Lei | Regina Barzilay | Tommi Jaakkola

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Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks
Hua He | Kevin Gimpel | Jimmy Lin

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Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen | Brendan O’Connor

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A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Shaohua Li | Jun Zhu | Chunyan Miao

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Reading Documents for Bayesian Online Change Point Detection
Taehoon Kim | Jaesik Choi

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Recognizing Textual Entailment Using Probabilistic Inference
Lei Sha | Sujian Li | Baobao Chang | Zhifang Sui | Tingsong Jiang

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Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks
Zhen Wang | Tingsong Jiang | Baobao Chang | Zhifang Sui

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Unsupervised Negation Focus Identification with Word-Topic Graph Model
Bowei Zou | Guodong Zhou | Qiaoming Zhu

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Reverse-engineering Language: A Study on the Semantic Compositionality of German Compounds
Corina Dima

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Event Detection and Factuality Assessment with Non-Expert Supervision
Kenton Lee | Yoav Artzi | Yejin Choi | Luke Zettlemoyer

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Large-Scale Acquisition of Entailment Pattern Pairs by Exploiting Transitivity
Julien Kloetzer | Kentaro Torisawa | Chikara Hashimoto | Jong-Hoon Oh

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Context-Dependent Knowledge Graph Embedding
Yuanfei Luo | Quan Wang | Bin Wang | Li Guo

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Learning to Identify the Best Contexts for Knowledge-based WSD
Evgenia Wasserman Pritsker | William Cohen | Einat Minkov

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Measuring Prerequisite Relations Among Concepts
Chen Liang | Zhaohui Wu | Wenyi Huang | C. Lee Giles

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Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
Nut Limsopatham | Nigel Collier

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Script Induction as Language Modeling
Rachel Rudinger | Pushpendre Rastogi | Francis Ferraro | Benjamin Van Durme

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Online Learning of Interpretable Word Embeddings
Hongyin Luo | Zhiyuan Liu | Huanbo Luan | Maosong Sun

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A Strong Lexical Matching Method for the Machine Comprehension Test
Ellery Smith | Nicola Greco | Matko Bošnjak | Andreas Vlachos

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Broad-coverage CCG Semantic Parsing with AMR
Yoav Artzi | Kenton Lee | Luke Zettlemoyer

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Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Pei-Hao Su | David Vandyke | Steve Young

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Do Multi-Sense Embeddings Improve Natural Language Understanding?
Jiwei Li | Dan Jurafsky

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Learning Semantic Composition to Detect Non-compositionality of Multiword Expressions
Majid Yazdani | Meghdad Farahmand | James Henderson

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Solving General Arithmetic Word Problems
Subhro Roy | Dan Roth

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Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Daojian Zeng | Kang Liu | Yubo Chen | Jun Zhao

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CORE: Context-Aware Open Relation Extraction with Factorization Machines
Fabio Petroni | Luciano Del Corro | Rainer Gemulla

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Improved Relation Extraction with Feature-Rich Compositional Embedding Models
Matthew R. Gormley | Mo Yu | Mark Dredze

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Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
Yan Xu | Lili Mou | Ge Li | Yunchuan Chen | Hao Peng | Zhi Jin

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A Computational Cognitive Model of Novel Word Generalization
Aida Nematzadeh | Erin Grant | Suzanne Stevenson

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Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources
Lucie Flekova | Iryna Gurevych

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Leave-one-out Word Alignment without Garbage Collector Effects
Xiaolin Wang | Masao Utiyama | Andrew Finch | Taro Watanabe | Eiichiro Sumita

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Generalized Agreement for Bidirectional Word Alignment
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu

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A Transition-based Model for Joint Segmentation, POS-tagging and Normalization
Tao Qian | Yue Zhang | Meishan Zhang | Yafeng Ren | Donghong Ji

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Multilingual discriminative lexicalized phrase structure parsing
Benoit Crabbé

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Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing
Yuan Zhang | Regina Barzilay

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Diversity in Spectral Learning for Natural Language Parsing
Shashi Narayan | Shay B. Cohen

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Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks
Xinchi Chen | Yaqian Zhou | Chenxi Zhu | Xipeng Qiu | Xuanjing Huang

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Turn-taking phenomena in incremental dialogue systems
Hatim Khouzaimi | Romain Laroche | Fabrice Lefèvre

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Hierarchical Latent Words Language Models for Robust Modeling to Out-Of Domain Tasks
Ryo Masumura | Taichi Asami | Takanobu Oba | Hirokazu Masataki | Sumitaka Sakauchi | Akinori Ito

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A Coarse-Grained Model for Optimal Coupling of ASR and SMT Systems for Speech Translation
Gaurav Kumar | Graeme Blackwood | Jan Trmal | Daniel Povey | Sanjeev Khudanpur

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Abstractive Multi-document Summarization with Semantic Information Extraction
Wei Li

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Concept-based Summarization using Integer Linear Programming: From Concept Pruning to Multiple Optimal Solutions
Florian Boudin | Hugo Mougard | Benoit Favre

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GhostWriter: Using an LSTM for Automatic Rap Lyric Generation
Peter Potash | Alexey Romanov | Anna Rumshisky

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Better Summarization Evaluation with Word Embeddings for ROUGE
Jun-Ping Ng | Viktoria Abrecht

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Krimping texts for better summarization
Marina Litvak | Mark Last | Natalia Vanetik

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From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes
Dimitra Gkatzia | Verena Rieser | Phil Bartie | William Mackaness

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An Unsupervised Bayesian Modelling Approach for Storyline Detection on News Articles
Deyu Zhou | Haiyang Xu | Yulan He

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Topical Coherence for Graph-based Extractive Summarization
Daraksha Parveen | Hans-Martin Ramsl | Michael Strube

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Summarizing Student Responses to Reflection Prompts
Wencan Luo | Diane Litman

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Extractive Summarization by Maximizing Semantic Volume
Dani Yogatama | Fei Liu | Noah A. Smith

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LCSTS: A Large Scale Chinese Short Text Summarization Dataset
Baotian Hu | Qingcai Chen | Fangze Zhu

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Discourse Planning with an N-gram Model of Relations
Or Biran | Kathleen McKeown

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Experiments with Generative Models for Dependency Tree Linearization
Richard Futrell | Edward Gibson

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Summarization Based on Embedding Distributions
Hayato Kobayashi | Masaki Noguchi | Taichi Yatsuka

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Reversibility reconsidered: finite-state factors for efficient probabilistic sampling in parsing and generation
Marc Dymetman | Sriram Venkatapathy | Chunyang Xiao

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A quantitative analysis of gender differences in movies using psycholinguistic normatives
Anil Ramakrishna | Nikolaos Malandrakis | Elizabeth Staruk | Shrikanth Narayanan

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EMNLP versus ACL: Analyzing NLP research over time
Sujatha Das Gollapalli | Xiaoli Li

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Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge
Yang Li | Peter Clark

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WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yi Yang | Wen-tau Yih | Christopher Meek

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Personalized Machine Translation: Predicting Translational Preferences
Shachar Mirkin | Jean-Luc Meunier

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Talking to the crowd: What do people react to in online discussions?
Aaron Jaech | Victoria Zayats | Hao Fang | Mari Ostendorf | Hannaneh Hajishirzi

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What Your Username Says About You
Aaron Jaech | Mari Ostendorf

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Knowledge Base Inference using Bridging Entities
Bhushan Kotnis | Pradeep Bansal | Partha P. Talukdar

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Specializing Word Embeddings for Similarity or Relatedness
Douwe Kiela | Felix Hill | Stephen Clark

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Evaluation of Word Vector Representations by Subspace Alignment
Yulia Tsvetkov | Manaal Faruqui | Wang Ling | Guillaume Lample | Chris Dyer

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Higher-order logical inference with compositional semantics
Koji Mineshima | Pascual Martínez-Gómez | Yusuke Miyao | Daisuke Bekki

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Any-language frame-semantic parsing
Anders Johannsen | Héctor Martínez Alonso | Anders Søgaard

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What’s in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation
Arne Köhn

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Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron
Jun Araki | Teruko Mitamura

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A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation
Rico Sennrich | Barry Haddow

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Variable-Length Word Encodings for Neural Translation Models
Rohan Chitnis | John DeNero

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A Binarized Neural Network Joint Model for Machine Translation
Jingyi Zhang | Masao Utiyama | Eiichiro Sumita | Graham Neubig | Satoshi Nakamura

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Bayesian Optimization of Text Representations
Dani Yogatama | Lingpeng Kong | Noah A. Smith

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A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
Hao Peng | Lili Mou | Ge Li | Yunchuan Chen | Yangyang Lu | Zhi Jin

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Efficient Hyper-parameter Optimization for NLP Applications
Lidan Wang | Minwei Feng | Bowen Zhou | Bing Xiang | Sridhar Mahadevan

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Improved Arabic Dialect Classification with Social Media Data
Fei Huang

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Exploiting Debate Portals for Semi-Supervised Argumentation Mining in User-Generated Web Discourse
Ivan Habernal | Iryna Gurevych

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Confounds and Consequences in Geotagged Twitter Data
Umashanthi Pavalanathan | Jacob Eisenstein

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Modeling Reportable Events as Turning Points in Narrative
Jessica Ouyang | Kathleen McKeown

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Towards the Extraction of Customer-to-Customer Suggestions from Reviews
Sapna Negi | Paul Buitelaar

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Using Content-level Structures for Summarizing Microblog Repost Trees
Jing Li | Wei Gao | Zhongyu Wei | Baolin Peng | Kam-Fai Wong

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Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition
Ryu Iida | Kentaro Torisawa | Chikara Hashimoto | Jong-Hoon Oh | Julien Kloetzer

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Estimation of Discourse Segmentation Labels from Crowd Data
Ziheng Huang | Jialu Zhong | Rebecca J. Passonneau

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Comparing Word Representations for Implicit Discourse Relation Classification
Chloé Braud | Pascal Denis

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Better Document-level Sentiment Analysis from RST Discourse Parsing
Parminder Bhatia | Yangfeng Ji | Jacob Eisenstein

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Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations
Yangfeng Ji | Gongbo Zhang | Jacob Eisenstein

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Wikification of Concept Mentions within Spoken Dialogues Using Domain Constraints from Wikipedia
Seokhwan Kim | Rafael E. Banchs | Haizhou Li

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Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition
Biao Zhang | Jinsong Su | Deyi Xiong | Yaojie Lu | Hong Duan | Junfeng Yao

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On the Role of Discourse Markers for Discriminating Claims and Premises in Argumentative Discourse
Judith Eckle-Kohler | Roland Kluge | Iryna Gurevych

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Fatal or not? Finding errors that lead to dialogue breakdowns in chat-oriented dialogue systems
Ryuichiro Higashinaka | Masahiro Mizukami | Kotaro Funakoshi | Masahiro Araki | Hiroshi Tsukahara | Yuka Kobayashi

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Learning Word Meanings and Grammar for Describing Everyday Activities in Smart Environments
Muhammad Attamimi | Yuji Ando | Tomoaki Nakamura | Takayuki Nagai | Daichi Mochihashi | Ichiro Kobayashi | Hideki Asoh

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Discourse Element Identification in Student Essays based on Global and Local Cohesion
Wei Song | Ruiji Fu | Lizhen Liu | Ting Liu

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Adapting Coreference Resolution for Narrative Processing
Quynh Ngoc Thi Do | Steven Bethard | Marie-Francine Moens

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Joint Lemmatization and Morphological Tagging with Lemming
Thomas Müller | Ryan Cotterell | Alexander Fraser | Hinrich Schütze

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Transducer Disambiguation with Sparse Topological Features
Gonzalo Iglesias | Adrià de Gispert | Bill Byrne

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Arabic Diacritization with Recurrent Neural Networks
Yonatan Belinkov | James Glass

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Automatic Diacritics Restoration for Hungarian
Attila Novák | Borbála Siklósi

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Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model
Hajime Morita | Daisuke Kawahara | Sadao Kurohashi

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Can Symbol Grounding Improve Low-Level NLP? Word Segmentation as a Case Study
Hirotaka Kameko | Shinsuke Mori | Yoshimasa Tsuruoka

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When Are Tree Structures Necessary for Deep Learning of Representations?
Jiwei Li | Thang Luong | Dan Jurafsky | Eduard Hovy

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Discriminative Neural Sentence Modeling by Tree-Based Convolution
Lili Mou | Hao Peng | Ge Li | Yan Xu | Lu Zhang | Zhi Jin

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Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents
Pengfei Liu | Xipeng Qiu | Xinchi Chen | Shiyu Wu | Xuanjing Huang

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Verbal and Nonverbal Clues for Real-life Deception Detection
Verónica Pérez-Rosas | Mohamed Abouelenien | Rada Mihalcea | Yao Xiao | CJ Linton | Mihai Burzo

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Social Media Text Classification under Negative Covariate Shift
Geli Fei | Bing Liu

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Co-Training for Topic Classification of Scholarly Data
Cornelia Caragea | Florin Bulgarov | Rada Mihalcea

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Humor Recognition and Humor Anchor Extraction
Diyi Yang | Alon Lavie | Chris Dyer | Eduard Hovy

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Topic Identification and Discovery on Text and Speech
Chandler May | Francis Ferraro | Alan McCree | Jonathan Wintrode | Daniel Garcia-Romero | Benjamin Van Durme

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A Dynamic Programming Algorithm for Computing N-gram Posteriors from Lattices
Doğan Can | Shrikanth Narayanan

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Bilingual Structured Language Models for Statistical Machine Translation
Ekaterina Garmash | Christof Monz

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Compact, Efficient and Unlimited Capacity: Language Modeling with Compressed Suffix Trees
Ehsan Shareghi | Matthias Petri | Gholamreza Haffari | Trevor Cohn

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ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation
Chuncheng Xiang | Tingsong Jiang | Baobao Chang | Zhifang Sui

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A Single Word is not Enough: Ranking Multiword Expressions Using Distributional Semantics
Martin Riedl | Chris Biemann

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Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining
Likun Qiu | Yue Zhang | Yanan Lu

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Navigating the Semantic Horizon using Relative Neighborhood Graphs
Amaru Cuba Gyllensten | Magnus Sahlgren

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Multi- and Cross-Modal Semantics Beyond Vision: Grounding in Auditory Perception
Douwe Kiela | Stephen Clark

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Automatic recognition of habituals: a three-way classification of clausal aspect
Annemarie Friedrich | Manfred Pinkal

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Distributed Representations for Unsupervised Semantic Role Labeling
Kristian Woodsend | Mirella Lapata

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A Tableau Prover for Natural Logic and Language
Lasha Abzianidze

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JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis
Kun-Hu Luo | Zhi-Hong Deng | Hongliang Yu | Liang-Chen Wei

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PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis
Thien Hai Nguyen | Kiyoaki Shirai

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ASTD: Arabic Sentiment Tweets Dataset
Mahmoud Nabil | Mohamed Aly | Amir Atiya

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Adjective Intensity and Sentiment Analysis
Raksha Sharma | Mohit Gupta | Astha Agarwal | Pushpak Bhattacharyya

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The Rating Game: Sentiment Rating Reproducibility from Text
Lasse Borgholt | Peter Simonsen | Dirk Hovy

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A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining
Salud M. Jiménez Zafra | Giacomo Berardi | Andrea Esuli | Diego Marcheggiani | María Teresa Martín-Valdivia | Alejandro Moreo Fernández

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Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis
Soujanya Poria | Erik Cambria | Alexander Gelbukh

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SLSA: A Sentiment Lexicon for Standard Arabic
Ramy Eskander | Owen Rambow

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Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification
Ning Xing | Yuexian Hou | Peng Zhang | Wenjie Li | Dawei Song

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That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets
William Yang Wang | Diyi Yang

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Detection of Steganographic Techniques on Twitter
Alex Wilson | Phil Blunsom | Andrew Ker

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#SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns
Dong Nguyen | Tijs van den Broek | Claudia Hauff | Djoerd Hiemstra | Michel Ehrenhard

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An Analysis of Domestic Abuse Discourse on Reddit
Nicolas Schrading | Cecilia Ovesdotter Alm | Ray Ptucha | Christopher Homan

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Twitter-scale New Event Detection via K-term Hashing
Dominik Wurzer | Victor Lavrenko | Miles Osborne

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Classifying Tweet Level Judgements of Rumours in Social Media
Michal Lukasik | Trevor Cohn | Kalina Bontcheva

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Identification and Verification of Simple Claims about Statistical Properties
Andreas Vlachos | Sebastian Riedel


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bib (full) Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

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Semantic Similarity Frontiers: From Concepts to Documents
David Jurgens | Mohammad Taher Pilehvar

Semantic similarity forms a central component in many NLP systems, from lexical semantics, to part of speech tagging, to social media analysis. Recent years have seen a renewed interest in developing new similarity techniques, buoyed in part by work on embeddings and by SemEval tasks in Semantic Textual Similarity and Cross-Level Semantic Similarity. The increased interest has led to hundreds of techniques for measuring semantic similarity, which makes it difficult for practitioners to identify which state-of-the-art techniques are applicable and easily integrated into projects and for researchers to identify which aspects of the problem require future research.This tutorial synthesizes the current state of the art for measuring semantic similarity for all types of conceptual or textual pairs and presents a broad overview of current techniques, what resources they use, and the particular inputs or domains to which the methods are most applicable. We survey methods ranging from corpus-based approaches operating on massive or domains-specific corpora to those leveraging structural information from expert-based or collaboratively-constructed lexical resources. Furthermore, we review work on multiple similarity tasks from sense-based comparisons to word, sentence, and document-sized comparisons and highlight general-purpose methods capable of comparing multiple types of inputs. Where possible, we also identify techniques that have been demonstrated to successfully operate in multilingual or cross-lingual settings.Our tutorial provides a clear overview of currently-available tools and their strengths for practitioners who need out of the box solutions and provides researchers with an understanding of the limitations of current state of the art and what open problems remain in the field. Given the breadth of available approaches, participants will also receive a detailed bibliography of approaches (including those not directly covered in the tutorial), annotated according to the approaches abilities, and pointers to when open-source implementations of the algorithms may be obtained.

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Personality Research for NLP
Yair Neuman

“Personality” is a psychological concept describing the individual's characteristic patterns of thought, emotion, and behavior. In the context of Big Data and granular analytics, it is highly important to measure the individual's personality dimensions as these may be used for various practical applications. However, personality has been traditionally studied by questionnaires and other forms of low tech methodologies. The availability of textual data and the development of powerful NLP technologies, invite the challenge of automatically measuring personality dimensions for various applications from granular analytics of customers to the forensic identification of potential offenders. While there are emerging attempts to address this challenge, these attempts almost exclusively focus on one theoretical model of personality and on classification tasks limited when tagged data are not available.The major aim of the tutorial is to provide NLP researchers with an introduction to personality theories that may empower their scope of research. In addition, two secondary aims are to survey some recent directions in computational personality and to point to future directions in which the field may be developed (e.g. Textual Entailment for Personality Analytics).

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Transparent Machine Learning for Information Extraction: State-of-the-Art and the Future
Laura Chiticariu | Yunyao Li | Frederick Reiss

The rise of Big Data analytics over unstructured text has led to renewed interest in information extraction (IE). These applications need effective IE as a first step towards solving end-to-end real world problems (e.g. biology, medicine, finance, media and entertainment, etc). Much recent NLP research has focused on addressing specific IE problems using a pipeline of multiple machine learning techniques. This approach requires an analyst with the expertise to answer questions such as: “What ML techniques should I combine to solve this problem?”; “What features will be useful for the composite pipeline?”; and “Why is my model giving the wrong answer on this document?”. The need for this expertise creates problems in real world applications. It is very difficult in practice to find an analyst who both understands the real world problem and has deep knowledge of applied machine learning. As a result, the real impact by current IE research does not match up to the abundant opportunities available.In this tutorial, we introduce the concept of transparent machine learning. A transparent ML technique is one that:- produces models that a typical real world use can read and understand;- uses algorithms that a typical real world user can understand; and- allows a real world user to adapt models to new domains.The tutorial is aimed at IE researchers in both the academic and industry communities who are interested in developing and applying transparent ML.

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Knowledge Acquisition for Web Search
Marius Pasca

The identification of textual items, or documents, that best match a user’s information need, as expressed in search queries, forms the core functionality of information retrieval systems. Well-known challenges are associated with understanding the intent behind user queries; and, more importantly, with matching inherently-ambiguous queries to documents that may employ lexically different phrases to convey the same meaning. The conversion of semi-structured content from Wikipedia and other resources into structured data produces knowledge potentially more suitable to database-style queries and, ideally, to use in information retrieval. In parallel, the availability of textual documents on the Web enables an aggressive push towards the automatic acquisition of various types of knowledge from text. Methods developed under the umbrella of open-domain information extraction acquire open-domain classes of instances and relations from Web text. The methods operate over unstructured or semi-structured text available within collections of Web documents, or over relatively more intriguing streams of anonymized search queries. Some of the methods import the automatically-extracted data into human-generated resources, or otherwise exploit existing human-generated resources. In both cases, the goal is to expand the coverage of the initial resources, thus providing information about more of the topics that people in general, and Web search users in particular, may be interested in.

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Learning Semantic Relations from Text
Preslav Nakov | Vivi Nastase | Diarmuid Ó Séaghdha | Stan Szpakowicz

Every non-trivial text describes interactions and relations between people, institutions, activities, events and so on. What we know about the world consists in large part of such relations, and that knowledge contributes to the understanding of what texts refer to. Newly found relations can in turn become part of this knowledge that is stored for future use.To grasp a text’s semantic content, an automatic system must be able to recognize relations in texts and reason about them. This may be done by applying and updating previously acquired knowledge. We focus here in particular on semantic relations which describe the interactions among nouns and compact noun phrases, and we present such relations from both a theoretical and a practical perspective. The theoretical exploration sketches the historical path which has brought us to the contemporary view and interpretation of semantic relations. We discuss a wide range of relation inventories proposed by linguists and by language processing people. Such inventories vary by domain, granularity and suitability for downstream applications.On the practical side, we investigate the recognition and acquisition of relations from texts. In a look at supervised learning methods, we present available datasets, the variety of features which can describe relation instances, and learning algorithms found appropriate for the task. Next, we present weakly supervised and unsupervised learning methods of acquiring relations from large corpora with little or no previously annotated data. We show how enduring the bootstrapping algorithm based on seed examples or patterns has proved to be, and how it has been adapted to tackle Web-scale text collections. We also show a few machine learning techniques which can perform fast and reliable relation extraction by taking advantage of data redundancy and variability.

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Applications of Social Media Text Analysis
Atefeh Farzindar | Diana Inkpen

Analyzing social media texts is a complex problem that becomes difficult to address using traditional Natural Language Processing (NLP) methods. Our tutorial focuses on presenting new methods for NLP tasks and applications that work on noisy and informal texts, such as the ones from social media.Automatic processing of large collections of social media texts is important because they contain a lot of useful information, due to the in-creasing popularity of all types of social media. Use of social media and messaging apps grew 203 percent year-on-year in 2013, with overall app use rising 115 percent over the same period, as reported by Statista, citing data from Flurry Analytics. This growth means that 1.61 billion people are now active in social media around the world and this is expected to advance to 2 billion users in 2016, led by India. The research shows that consumers are now spending daily 5.6 hours on digital media including social media and mo-bile internet usage.At the heart of this interest is the ability for users to create and share content via a variety of platforms such as blogs, micro-blogs, collaborative wikis, multimedia sharing sites, social net-working sites. The unprecedented volume and variety of user-generated content, as well as the user interaction network constitute new opportunities for understanding social behavior and building socially intelligent systems. Therefore it is important to investigate methods for knowledge extraction from social media data. Furthermore, we can use this information to detect and retrieve more related content about events, such as photos and video clips that have caption texts.

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Robust Semantic Analysis of Multiword Expressions with FrameNet
Miriam R. L. Petruck | Valia Kordoni

This tutorial will give participants a solid understanding of the linguistic features of multiword expressions (MWEs), focusing on the semantics of such expressions and their importance for natural language processing and language technology, with particular attention to the way that FrameNet (framenet.icsi.berkeley.edu) handles this wide spread phenomenon. Our target audience includes researchers and practitioners of language technology, not necessarily experts in MWEs or knowledgeable about FrameNet, who are interested in NLP tasks that involve or could benefit from considering MWEs as a pervasive phenomenon in human language and communication.NLP research has been interested in automatic processing of multiword expressions, with reports on and tasks relating to such efforts presented at workshops and conferences for at least ten years (e.g. ACL 2003, LREC 2008, COLING 2010, EACL 2014). Overcoming the challenge of automatically processing MWEs remains elusive in part because of the difficulty in recognizing, acquiring, and interpreting such forms.Indeed the phenomenon manifests in a range of linguistic forms (as Sag et al. (2001), among many others, have documented), including: noun + noun compounds (e.g. fish knife, health hazard etc.); adjective + noun compounds (e.g. political agenda, national interest, etc.); particle verbs (shut up, take out, etc.); prepositional verbs (e.g. look into, talk into, etc.); VP idioms, such as kick the bucket, and pull someone’s leg, along with less obviously idiomatic forms like answer the door, mention someone’s name, etc.; expressions that have their own mini-grammars, such as names with honorifics and terms of address (e.g. Rabbi Lord Jonathan Sacks), kinship terms (e.g. second cousin once removed), and time expressions (e.g. January 9, 2015); support verb constructions (e.g. verbs: take a bath, make a promise, etc; and prepositions: in doubt, under review, etc.). Linguists address issues of polysemy, compositionality, idiomaticity, and continuity for each type included here.While native speakers use these forms with ease, the treatment and interpretation of MWEs in computational systems requires considerable effort due to the very issues that concern linguists.

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Computational Analysis of Affect and Emotion in Language
Saif Mohammad | Cecilia Ovesdotter Alm

Computational linguistics has witnessed a surge of interest in approaches to emotion and affect analysis, tackling problems that extend beyond sentiment analysis in depth and complexity. This area involves basic emotions (such as joy, sadness, and fear) as well as any of the hundreds of other emotions humans are capable of (such as optimism, frustration, and guilt), expanding into affective conditions, experiences, and activities. Leveraging linguistic data for computational affect and emotion inference enables opportunities to address a range of affect-related tasks, problems, and non-invasive applications that capture aspects essential to the human condition and individuals’ cognitive processes. These efforts enable and facilitate human-centered computing experiences, as demonstrated by applications across clinical, socio-political, artistic, educational, and commercial domains. Efforts to computationally detect, characterize, and generate emotions or affect-related phenomena respond equally to technological needs for personalized, micro-level analytics and broad-coverage, macro-level inference, and they have involved both small and massive amounts of data.While this is an exciting area with numerous opportunities for members of the ACL community, a major obstacle is its intersection with other investigatory traditions, necessitating knowledge transfer. This tutorial comprehensively integrates relevant concepts and frameworks from linguistics, cognitive science, affective computing, and computational linguistics in order to equip researchers and practitioners with the adequate background and knowledge to work effectively on problems and tasks either directly involving, or benefiting from having an understanding of, affect and emotion analysis.There is a substantial body of work in traditional sentiment analysis focusing on positive and negative sentiment. This tutorial covers approaches and features that migrate well to affect analysis. We also discuss key differences from sentiment analysis, and their implications for analyzing affect and emotion.The tutorial begins with an introduction that highlights opportunities, key terminology, and interesting tasks and challenges (1). The body of the tutorial covers characteristics of emotive language use with emphasis on relevance for computational analysis (2); linguistic data—from conceptual analysis frameworks via useful existing resources to important annotation topics (3); computational approaches for lexical semantic emotion analysis (4); computational approaches for emotion and affect analysis in text (5); visualization methods (6); and a survey of application areas with affect-related problems (7). The tutorial concludes with an outline of future directions and a discussion with participants about the areas relevant to their respective tasks of interest (8).Besides attending the tutorial, tutorial participants receive electronic copies of tutorial slides, a complete reference list, as well as a categorized annotated bibliography that concentrates on seminal works, recent important publications, and other products and resources for researchers and developers.