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Front matter |
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SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment Marco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini and Roberto Zamparelli |
pp. 1–8 |
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SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue Systems Ioannis Klasinas, Elias Iosif, Katerina Louka and Alexandros Potamianos |
pp. 9–16 |
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SemEval-2014 Task 3: Cross-Level Semantic Similarity David Jurgens, Mohammad Taher Pilehvar and Roberto Navigli |
pp. 17–26 |
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SemEval-2014 Task 4: Aspect Based Sentiment Analysis Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos and Suresh Manandhar |
pp. 27–35 |
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SemEval 2014 Task 5 - L2 Writing Assistant Maarten van Gompel, Iris Hendrickx, Antal van den Bosch, Els Lefever and Veronique Hoste |
pp. 36–44 |
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SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial Commands Kais Dukes |
pp. 45–53 |
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SemEval-2014 Task 7: Analysis of Clinical Text Sameer Pradhan, Noémie Elhadad, Wendy Chapman, Suresh Manandhar and Guergana Savova |
pp. 54–62 |
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SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Jan Hajic, Angelina Ivanova and Yi Zhang |
pp. 63–72 |
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SemEval-2014 Task 9: Sentiment Analysis in Twitter Sara Rosenthal, Alan Ritter, Preslav Nakov and Veselin Stoyanov |
pp. 73–80 |
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SemEval-2014 Task 10: Multilingual Semantic Textual Similarity Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau and Janyce Wiebe |
pp. 81–91 |
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AI-KU: Using Co-Occurrence Modeling for Semantic Similarity Osman Baskaya |
pp. 92–96 |
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Alpage: Transition-based Semantic Graph Parsing with Syntactic Features Corentin Ribeyre, Eric Villemonte de la Clergerie and Djamé Seddah |
pp. 97–103 |
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ASAP: Automatic Semantic Alignment for Phrases Ana Alves, Adriana Ferrugento, Mariana Lourenço and Filipe Rodrigues |
pp. 104–108 |
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AT&T: The Tag&Parse Approach to Semantic Parsing of Robot Spatial Commands Svetlana Stoyanchev, Hyuckchul Jung, John Chen and Srinivas Bangalore |
pp. 109–113 |
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AUEB: Two Stage Sentiment Analysis of Social Network Messages Rafael - Michael Karampatsis, John Pavlopoulos and Prodromos Malakasiotis |
pp. 114–118 |
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Bielefeld SC: Orthonormal Topic Modelling for Grammar Induction John Philip McCrae and Philipp Cimiano |
pp. 119–122 |
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Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble Nádia Silva, Estevam Hruschka and Eduardo Hruschka |
pp. 123–128 |
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Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble Nádia Silva, Estevam Hruschka and Eduardo Hruschka |
pp. 129–134 |
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BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework Sérgio Matos, Tiago Nunes and José Luís Oliveira |
pp. 135–139 |
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Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014 Pavel Blinov and Eugeny Kotelnikov |
pp. 140–144 |
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BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment Saul Leon, Darnes Vilariño, David Pinto, Mireya Tovar and Beatriz Beltrán |
pp. 145–148 |
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BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity Darnes Vilariño, David Pinto, Saul Leon, Mireya Tovar and Beatriz Beltrán |
pp. 149–153 |
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BUAP: Polarity Classification of Short Texts David Pinto, Darnes Vilariño, Saul Leon, Miguel Jasso and Cupertino Lucero |
pp. 154–159 |
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CECL: a New Baseline and a Non-Compositional Approach for the Sick Benchmark Yves Bestgen |
pp. 160–165 |
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CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features João Leal, Sara Pinto, Ana Bento, Hugo Gonçalo Oliveira and Paulo Gomes |
pp. 166–170 |
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Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets Pablo Gamallo and Marcos Garcia |
pp. 171–175 |
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CMU: Arc-Factored, Discriminative Semantic Dependency Parsing Sam Thomson, Brendan O’Connor, Jeffrey Flanigan, David Bamman, Jesse Dodge, Swabha Swayamdipta, Nathan Schneider, Chris Dyer and Noah A. Smith |
pp. 176–180 |
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CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning Kamla Al-Mannai, Hanan Alshikhabobakr, Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor and Behrang Mohit |
pp. 181–185 |
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CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor and Behrang Mohit |
pp. 186–191 |
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CNRC-TMT: Second Language Writing Assistant System Description Cyril Goutte, Michel Simard and Marine Carpuat |
pp. 192–197 |
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Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media Sara Rosenthal, Kathy McKeown and Apoorv Agarwal |
pp. 198–202 |
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COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis Kim Schouten, Flavius Frasincar and Franciska de Jong |
pp. 203–207 |
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Coooolll: A Deep Learning System for Twitter Sentiment Classification Duyu Tang, Furu Wei, Bing Qin, Ting Liu and Ming Zhou |
pp. 208–212 |
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Copenhagen-Malmö: Tree Approximations of Semantic Parsing Problems Natalie Schluter, Anders Søgaard, Jakob Elming, Dirk Hovy, Barbara Plank, Héctor Martínez Alonso, Anders Johanssen and Sigrid Klerke |
pp. 213–217 |
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DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter Julio Villena-Román, Janine García-Morera and José Carlos González-Cristóbal |
pp. 218–222 |
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DCU: Aspect-based Polarity Classification for SemEval Task 4 Joachim Wagner, Piyush Arora, Santiago Cortes, Utsab Barman, Dasha Bogdanova, Jennifer Foster and Lamia Tounsi |
pp. 223–229 |
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DIT: Summarisation and Semantic Expansion in Evaluating Semantic Similarity Magdalena Kacmajor and John D. Kelleher |
pp. 230–234 |
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DLIREC: Aspect Term Extraction and Term Polarity Classification System Zhiqiang Toh and Wenting Wang |
pp. 235–240 |
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DLS@CU: Sentence Similarity from Word Alignment Md Arafat Sultan, Steven Bethard and Tamara Sumner |
pp. 241–246 |
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Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps Ted Pedersen |
pp. 247–251 |
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ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification Fangxi Zhang, Zhihua Zhang and Man Lan |
pp. 252–258 |
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ECNU: Expression- and Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features Jiang Zhao, Man Lan and Tiantian Zhu |
pp. 259–264 |
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ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation Tiantian Zhu and Man Lan |
pp. 265–270 |
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ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment Jiang Zhao, Tiantian Zhu and Man Lan |
pp. 271–277 |
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ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes Parth Pathak, Pinal Patel, Vishal Panchal, Narayan Choudhary, Amrish Patel and Gautam Joshi |
pp. 278–283 |
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FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic Similarity Ngoc Phuoc An Vo, Tommaso Caselli and Octavian Popescu |
pp. 284–288 |
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FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTE Ngoc Phuoc An Vo, Octavian Popescu and Tommaso Caselli |
pp. 289–293 |
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GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams Javi Fernández, Yoan Gutiérrez, Jose Manuel Gómez and Patricio Martinez-Barco |
pp. 294–299 |
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haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTE Lorenzo Ferrone and Fabio Massimo Zanzotto |
pp. 300–304 |
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HulTech: A General Purpose System for Cross-Level Semantic Similarity based on Anchor Web Counts Jose G. Moreno, Rumen Moraliyski, Asma Berrezoug and Gaël Dias |
pp. 305–308 |
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IHS R&D Belarus: Cross-domain extraction of product features using CRF Maryna Chernyshevich |
pp. 309–313 |
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IITP: A Supervised Approach for Disorder Mention Detection and Disambiguation Utpal Kumar Sikdar, Asif Ekbal and Sriparna Saha |
pp. 314–318 |
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IITP: Supervised Machine Learning for Aspect based Sentiment Analysis Deepak Kumar Gupta and Asif Ekbal |
pp. 319–323 |
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IITPatna: Supervised Approach for Sentiment Analysis in Twitter Raja Selvarajan and Asif Ekbal |
pp. 324–328 |
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Illinois-LH: A Denotational and Distributional Approach to Semantics Alice Lai and Julia Hockenmaier |
pp. 329–334 |
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In-House: An Ensemble of Pre-Existing Off-the-Shelf Parsers Yusuke Miyao, Stephan Oepen and Daniel Zeman |
pp. 335–340 |
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Indian Institute of Technology-Patna: Sentiment Analysis in Twitter VIKRAM SINGH, Arif Md. Khan and Asif Ekbal |
pp. 341–345 |
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INSIGHT Galway: Syntactic and Lexical Features for Aspect Based Sentiment Analysis Sapna Negi and Paul Buitelaar |
pp. 346–350 |
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iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries Fritjof Bornebusch, Glaucia Cancino, Melanie Diepenbeck, Rolf Drechsler, Smith Djomkam, Alvine Nzeungang Fanseu, Maryam Jalali, Marc Michael, Jamal Mohsen, Max Nitze, Christina Plump, Mathias Soeken, Fred Tchambo, Toni and Henning Ziegler |
pp. 351–355 |
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IUCL: Combining Information Sources for SemEval Task 5 Alex Rudnick, Levi King, Can Liu, Markus Dickinson and Sandra Kübler |
pp. 356–360 |
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IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical Texts Koldo Gojenola, Maite Oronoz, Alicia Perez and Arantza Casillas |
pp. 361–365 |
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JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest Oliver Dürr, Fatih Uzdilli and Mark Cieliebak |
pp. 366–369 |
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JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis Braja Gopal Patra, Soumik Mandal, Dipankar Das and Sivaji Bandyopadhyay |
pp. 370–374 |
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JU-Evora: A Graph Based Cross-Level Semantic Similarity Analysis using Discourse Information Swarnendu Ghosh, Nibaran Das, Teresa Gonçalves and Paulo Quaresma |
pp. 375–379 |
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Kea: Sentiment Analysis of Phrases Within Short Texts Ameeta Agrawal and Aijun An |
pp. 380–384 |
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KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context Willem Mattelaer, Mathias Verbeke and Davide Nitti |
pp. 385–390 |
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KUNLPLab:Sentiment Analysis on Twitter Data Beakal Gizachew Assefa |
pp. 391–394 |
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Linköping: Cubic-Time Graph Parsing with a Simple Scoring Scheme Marco Kuhlmann |
pp. 395–399 |
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LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient Davide Buscaldi, Jorge García Flores, Joseph Le Roux, Nadi Tomeh and Belém Priego Sanchez |
pp. 400–405 |
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LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set Cynthia Van Hee, Marjan Van de Kauter, Orphee De Clercq, Els Lefever and Veronique Hoste |
pp. 406–410 |
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LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English David Vilares, Miguel Hermo, Miguel A. Alonso, Carlos Gómez-Rodríguez and Yerai Doval |
pp. 411–415 |
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Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems Abhay Kashyap, Lushan Han, Roberto Yus, Jennifer Sleeman, Taneeya Satyapanich, Sunil Gandhi and Tim Finin |
pp. 416–423 |
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MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7 Alejandro Riveros, Maria De Arteaga, Fabio González, Sergio Jimenez and Henning Müller |
pp. 424–427 |
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NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages Pedro Balage Filho, Lucas Avanço, Thiago Pardo and Maria das Graças Volpe Nunes |
pp. 428–432 |
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NILC_USP: Aspect Extraction using Semantic Labels Pedro Balage Filho and Thiago Pardo |
pp. 433–436 |
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NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry and Saif Mohammad |
pp. 437–442 |
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NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets Xiaodan Zhu, Svetlana Kiritchenko and Saif Mohammad |
pp. 443–447 |
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NTNU: Measuring Semantic Similarity with Sublexical Feature Representations and Soft Cardinality André Lynum, Partha Pakray, Björn Gambäck and Sergio Jimenez |
pp. 448–453 |
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OPI: Semeval-2014 Task 3 System Description Marek Kozlowski |
pp. 454–458 |
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Peking: Profiling Syntactic Tree Parsing Techniques for Semantic Graph Parsing Yantao Du, Fan Zhang, Weiwei Sun and Xiaojun Wan |
pp. 459–464 |
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Potsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic Parsing Željko Agić and Alexander Koller |
pp. 465–470 |
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Priberam: A Turbo Semantic Parser with Second Order Features André F. T. Martins and Mariana S. C. Almeida |
pp. 471–476 |
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RelAgent: Entity Detection and Normalization for Diseases in Clinical Records: a Linguistically Driven Approach SV Ramanan and Senthil Nathan |
pp. 477–481 |
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RoBox: CCG with Structured Perceptron for Supervised Semantic Parsing of Robotic Spatial Commands Kilian Evang and Johan Bos |
pp. 482–486 |
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RTM-DCU: Referential Translation Machines for Semantic Similarity Ergun Bicici and Andy Way |
pp. 487–496 |
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RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages Tobias Günther, Jean Vancoppenolle and Richard Johansson |
pp. 497–502 |
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SA-UZH: Verb-based Sentiment Analysis Nora Hollenstein, Michael Amsler, Martina Bachmann and Manfred Klenner |
pp. 503–507 |
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SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule Similarity Kalliopi Zervanou, Nikolaos Malandrakis and Shrikanth Narayanan |
pp. 508–511 |
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SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features Nikolaos Malandrakis, Michael Falcone, Colin Vaz, Jesse James Bisogni, Alexandros Potamianos and Shrikanth Narayanan |
pp. 512–516 |
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SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis Naveen Nandan, Daniel Dahlmeier, Akriti Vij and Nishtha Malhotra |
pp. 517–521 |
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SAP-RI: Twitter Sentiment Analysis in Two Days Akriti Vij, Nishta Malhotra, Naveen Nandan and Daniel Dahlmeier |
pp. 522–526 |
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SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis Pengfei Liu and Helen Meng |
pp. 527–531 |
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SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight Matching Thomas Proisl, Stefan Evert, Paul Greiner and Besim Kabashi |
pp. 532–540 |
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Sensible: L2 Translation Assistance by Emulating the Manual Post-Editing Process Liling Tan, Anne Schumann, Jose Martinez and Francis Bond |
pp. 541–545 |
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Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9 José Saias |
pp. 546–550 |
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SentiKLUE: Updating a Polarity Classifier in 48 Hours Stefan Evert, Thomas Proisl, Paul Greiner and Besim Kabashi |
pp. 551–555 |
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ShrdLite: Semantic Parsing Using a Handmade Grammar Peter Ljunglöf |
pp. 556–559 |
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SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity Carmen Banea, Di Chen, Rada Mihalcea, Claire Cardie and Janyce Wiebe |
pp. 560–565 |
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SINAI: Voting System for Aspect Based Sentiment Analysis Salud María Jiménez-Zafra, Eugenio Martínez-Cámara, Maite Martin and L. Alfonso Urena Lopez |
pp. 566–571 |
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SINAI: Voting System for Twitter Sentiment Analysis Eugenio Martínez-Cámara, Salud María Jiménez-Zafra, Maite Martin and L. Alfonso Urena Lopez |
pp. 572–577 |
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SNAP: A Multi-Stage XML-Pipeline for Aspect Based Sentiment Analysis Clemens Schulze Wettendorf, Robin Jegan, Allan Körner, Julia Zerche, Nataliia Plotnikova, Julian Moreth, Tamara Schertl, Verena Obermeyer, Susanne Streil, Tamara Willacker and Stefan Evert |
pp. 578–584 |
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SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity Pingping Huang and Baobao Chang |
pp. 585–589 |
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SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter Boris Velichkov, Borislav Kapukaranov, Ivan Grozev, Jeni Karanesheva, Todor Mihaylov, Yasen Kiprov, Preslav Nakov, Ivan Koychev and Georgi Georgiev |
pp. 590–595 |
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Supervised Methods for Aspect-Based Sentiment Analysis Hussam Hamdan, Patrice Bellot and Frederic Bechet |
pp. 596–600 |
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Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams Martin Jaggi, Fatih Uzdilli and Mark Cieliebak |
pp. 601–604 |
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Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment Analysis Alexandre Denis, Samuel Cruz-Lara, Nadia Bellalem and Lotfi Bellalem |
pp. 605–609 |
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SZTE-NLP: Aspect level opinion mining exploiting syntactic cues Viktor Hangya, Gabor Berend, István Varga and Richárd Farkas |
pp. 610–614 |
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SZTE-NLP: Clinical Text Analysis with Named Entity Recognition Melinda Katona and Richárd Farkas |
pp. 615–618 |
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TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach Arun kumar Jayapal, Martin Emms and John Kelleher |
pp. 619–623 |
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Team Z: Wiktionary as a L2 Writing Assistant Anubhav Gupta |
pp. 624–627 |
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TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data Yasuhide Miura, Shigeyuki Sakaki, Keigo Hattori and Tomoko Ohkuma |
pp. 628–632 |
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TeamZ: Measuring Semantic Textual Similarity for Spanish Using an Overlap-Based Approach Anubhav Gupta |
pp. 633–635 |
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The Impact of Z_score on Twitter Sentiment Analysis Hussam Hamdan, Patrice Bellot and Frederic Bechet |
pp. 636–641 |
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The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity Johannes Bjerva, Johan Bos, Rob van der Goot and Malvina Nissim |
pp. 642–646 |
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Think Positive: Towards Twitter Sentiment Analysis from Scratch Cicero dos Santos |
pp. 647–651 |
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ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics Ankur Parikh, Avinesh PVS, Joy Mustafi, Lalit Agarwalla and Ashish Mungi |
pp. 652–656 |
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TJP: Identifying the Polarity of Tweets from Contexts Tawunrat Chalothorn and Jeremy Ellman |
pp. 657–662 |
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TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7 Jitendra Jonnagaddala, Manish Kumar, Hong-Jie Dai, Enny Rachmani and Chien-Yeh Hsu |
pp. 663–667 |
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tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection Arodami Chorianopoulou, Georgia Athanasopoulou, Elias Iosif, Ioannis Klasinas and Alexandros Potamianos |
pp. 668–672 |
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TUGAS: Exploiting unlabelled data for Twitter sentiment analysis Silvio Amir, Miguel B. Almeida, Bruno Martins, João Filgueiras and Mario J. Silva |
pp. 673–677 |
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Turku: Broad-Coverage Semantic Parsing with Rich Features Jenna Kanerva, Juhani Luotolahti and Filip Ginter |
pp. 678–682 |
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UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis Viktor Pekar, Naveed Afzal and Bernd Bohnet |
pp. 683–687 |
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UEdin: Translating L1 Phrases in L2 Context using Context-Sensitive SMT Eva Hasler |
pp. 688–693 |
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ÚFAL: Using Hand-crafted Rules in Aspect Based Sentiment Analysis on Parsed Data Kateřina Veselovská and Aleš Tamchyna |
pp. 694–698 |
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UIO-Lien: Entailment Recognition using Minimal Recursion Semantics Elisabeth Lien and Milen Kouylekov |
pp. 699–703 |
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UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data Lucie Flekova, Oliver Ferschke and Iryna Gurevych |
pp. 704–710 |
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ULisboa: Identification and Classification of Medical Concepts André Leal, Diogo Gonçalves, Bruno Martins and Francisco M Couto |
pp. 711–715 |
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UMCC_DLSI_SemSim: Multilingual System for Measuring Semantic Textual Similarity Alexander Chavez, Héctor Dávila, Yoan Gutiérrez, Antonio Fernández-Orquín, Andrés Montoyo and Rafael Muñoz |
pp. 716–721 |
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UMCC_DLSI: A Probabilistic Automata for Aspect Based Sentiment Analysis Yenier Castañeda, Armando Collazo, Elvis Crego, Jorge L. Garcia, Yoan Gutierrez, David Tomás, Andrés Montoyo and Rafael Muñoz |
pp. 722–726 |
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UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity Pedro Aniel Sánchez-Mirabal, Yarelis Ruano Torres, Suilen Hernández Alvarado, Yoan Gutiérrez, Andrés Montoyo and Rafael Muñoz |
pp. 727–731 |
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UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment Sergio Jimenez, George Dueñas, Julia Baquero and Alexander Gelbukh |
pp. 732–742 |
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UNAL-NLP: Cross-Lingual Phrase Sense Disambiguation with Syntactic Dependency Trees Emilio Silva-Schlenker, Sergio Jimenez and Julia Baquero |
pp. 743–747 |
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UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity Pierpaolo Basile, Annalina Caputo and Giovanni Semeraro |
pp. 748–753 |
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UniPi: Recognition of Mentions of Disorders in Clinical Text Giuseppe Attardi, Vittoria Cozza and Daniele Sartiano |
pp. 754–760 |
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UNITOR: Aspect Based Sentiment Analysis with Structured Learning Giuseppe Castellucci, Simone Filice, Danilo Croce and Roberto Basili |
pp. 761–767 |
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University_of_Warwick: SENTIADAPTRON - A Domain Adaptable Sentiment Analyser for Tweets - Meets SemEval Richard Townsend, Aaron Kalair, Ojas Kulkarni, Rob Procter and Maria Liakata |
pp. 768–772 |
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UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment Resource Reynier Ortega Bueno, Adrian Fonseca Bruzón, Carlos Muñiz Cuza, Yoan Gutiérrez and Andres Montoyo |
pp. 773–778 |
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UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity Miguel Rios |
pp. 779–784 |
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UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment Rohit Gupta, Hanna Bechara, Ismail El Maarouf and Constantin Orasan |
pp. 785–789 |
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USF: Chunking for Aspect-term Identification & Polarity Classification Cindi Thompson |
pp. 790–795 |
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UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic Islam Beltagy, Stephen Roller, Gemma Boleda, Katrin Erk and Raymond Mooney |
pp. 796–801 |
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UTH_CCB: A report for SemEval 2014 – Task 7 Analysis of Clinical Text Yaoyun Zhang, Jingqi Wang, Buzhou Tang, Yonghui Wu, Min Jiang, Yukun Chen and Hua Xu |
pp. 802–806 |
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UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations Suwisa Kaewphan, Kai Hakala and Filip Ginter |
pp. 807–811 |
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UW-MRS: Leveraging a Deep Grammar for Robotic Spatial Commands Woodley Packard |
pp. 812–816 |
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UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis Tomáš Brychcín, Michal Konkol and Josef Steinberger |
pp. 817–822 |
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UWM: Applying an Existing Trainable Semantic Parser to Parse Robotic Spatial Commands Rohit Kate |
pp. 823–827 |
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UWM: Disorder Mention Extraction from Clinical Text Using CRFs and Normalization Using Learned Edit Distance Patterns Omid Ghiasvand and Rohit Kate |
pp. 828–832 |
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V3: Unsupervised Generation of Domain Aspect Terms for Aspect Based Sentiment Analysis Aitor García Pablos, Montse Cuadros and German Rigau |
pp. 833–837 |
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XRCE: Hybrid Classification for Aspect-based Sentiment Analysis Caroline Brun, Diana Nicoleta Popa and Claude Roux |
pp. 838–842 |
Last modified on August 6, 2014, 7:25 a.m.