Table of Contents

• Front Matter [doi]
• SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual EntailmentMarco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini, Roberto Zamparelli. 1-8 [doi]
• SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue SystemsIoannis Klasinas, Elias Iosif, Katerina Louka, Alexandros Potamianos. 9-16 [doi]
• SemEval-2014 Task 3: Cross-Level Semantic SimilarityDavid Jurgens, Mohammad Taher Pilehvar, Roberto Navigli. 17-26 [doi]
• SemEval-2014 Task 4: Aspect Based Sentiment AnalysisMaria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar. 27-35 [doi]
• SemEval 2014 Task 5 - L2 Writing AssistantMaarten van Gompel, Iris Hendrickx, Antal van den Bosch, Els Lefever, Véronique Hoste. 36-44 [doi]
• SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial CommandsKais Dukes. 45-53 [doi]
• SemEval-2014 Task 7: Analysis of Clinical TextSameer Pradhan, Noémie Elhadad, Wendy W. Chapman, Suresh Manandhar, Guergana Savova. 54-62 [doi]
• SemEval 2014 Task 8: Broad-Coverage Semantic Dependency ParsingStephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Jan Hajic, Angelina Ivanova, Yi Zhang 0003. 63-72 [doi]
• SemEval-2014 Task 9: Sentiment Analysis in TwitterSara Rosenthal, Alan Ritter, Preslav Nakov, Veselin Stoyanov. 73-80 [doi]
• SemEval-2014 Task 10: Multilingual Semantic Textual SimilarityEneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, Janyce Wiebe. 81-91 [doi]
• AI-KU: Using Co-Occurrence Modeling for Semantic SimilarityOsman Baskaya. 92-96 [doi]
• Alpage: Transition-based Semantic Graph Parsing with Syntactic FeaturesCorentin Ribeyre, Éric Villemonte de la Clergerie, Djamé Seddah. 97-103 [doi]
• ASAP: Automatic Semantic Alignment for PhrasesAna Oliveira Alves, Adriana Ferrugento, Mariana Lourenço, Filipe Rodrigues. 104-108 [doi]
• AT&T: The Tag&Parse Approach to Semantic Parsing of Robot Spatial CommandsSvetlana Stoyanchev, Hyuckchul Jung, John Chen, Srinivas Bangalore. 109-113 [doi]
• AUEB: Two Stage Sentiment Analysis of Social Network MessagesRafael-Michael Karampatsis, John Pavlopoulos, Prodromos Malakasiotis. 114-118 [doi]
• Bielefeld SC: Orthonormal Topic Modelling for Grammar InductionJohn Philip McCrae, Philipp Cimiano. 119-122 [doi]
• Biocom_Usp: Tweet Sentiment Analysis with Adaptive Boosting EnsembleNádia Félix F. da Silva, Estevam R. Hruschka Jr., Eduardo R. Hruschka. 123-128 [doi]
• Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble129-134 [doi]
• BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing FrameworkSérgio Matos, Tiago Nunes, José Luís Oliveira. 135-139 [doi]
• Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014Pavel Blinov, Eugeny Kotelnikov. 140-144 [doi]
• BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual EntailmentSaúl León, Darnes Vilariño, David Pinto, Mireya Tovar, Beatríz Beltrán. 145-148 [doi]
• BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual SimilarityDarnes Vilariño, David Pinto, Saúl León, Mireya Tovar, Beatríz Beltrán. 149-153 [doi]
• BUAP: Polarity Classification of Short TextsDavid Pinto, Darnes Vilariño, Saúl León, Miguel Jasso-Hernández, Cupertino Lucero. 154-159 [doi]
• CECL: a New Baseline and a Non-Compositional Approach for the Sick BenchmarkYves Bestgen. 160-165 [doi]
• CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of FeaturesJoão Leal, Sara Pinto, Ana Bento, Hugo Gonçalo Oliveira, Paulo Gomes. 166-170 [doi]
• Citius: A Naive-Bayes Strategy for Sentiment Analysis on English TweetsPablo Gamallo, Marcos García. 171-175 [doi]
• CMU: Arc-Factored, Discriminative Semantic Dependency ParsingSam Thomson, Brendan O'Connor, Jeffrey Flanigan, David Bamman, Jesse Dodge, Swabha Swayamdipta, Nathan Schneider, Chris Dyer, Noah A. Smith. 176-180 [doi]
• CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter TuningKamla Al-Mannai, Hanan Alshikhabobakr, Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor, Behrang Mohit. 181-185 [doi]
• CMUQ$@$Qatar: Using Rich Lexical Features for Sentiment Analysis on TwitterSabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor, Behrang Mohit. 186-191 [doi]
• CNRC-TMT: Second Language Writing Assistant System DescriptionCyril Goutte, Michel Simard, Marine Carpuat. 192-197 [doi]
• Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social MediaSara Rosenthal, Kathy McKeown, Apoorv Agarwal. 198-202 [doi]
• COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment AnalysisKim Schouten, Flavius Frasincar, Franciska de Jong. 203-207 [doi]
• Coooolll: A Deep Learning System for Twitter Sentiment ClassificationDuyu Tang, Furu Wei, Bing Qin, Ting Liu, Ming Zhou. 208-212 [doi]
• Copenhagen-Malmö: Tree Approximations of Semantic Parsing ProblemsNatalie Schluter, Anders Søgaard, Jakob Elming, Dirk Hovy, Barbara Plank, Héctor Martínez Alonso, Anders Johannsen, Sigrid Klerke. 213-217 [doi]
• DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in TwitterJulio Villena-Román, Janine García-Morera, José Carlos González Cristóbal. 218-222 [doi]
• DCU: Aspect-based Polarity Classification for SemEval Task 4Joachim Wagner, Piyush Arora, Santiago Cortes, Utsab Barman, Dasha Bogdanova, Jennifer Foster, Lamia Tounsi. 223-229 [doi]
• DIT: Summarisation and Semantic Expansion in Evaluating Semantic SimilarityMagdalena Kacmajor, John D. Kelleher. 230-234 [doi]
• DLIREC: Aspect Term Extraction and Term Polarity Classification SystemZhiqiang Toh, Wenting Wang. 235-240 [doi]
• DLS$@$CU: Sentence Similarity from Word AlignmentMd. Arafat Sultan, Steven Bethard, Tamara Sumner. 241-246 [doi]
• Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary OverlapsTed Pedersen. 247-251 [doi]
• ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity ClassificationFangxi Zhang, Zhihua Zhang, Man Lan. 252-258 [doi]
• ECNU: Expression- and Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective FeaturesJiang Zhao, Man Lan, Tiantian Zhu. 259-264 [doi]
• ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity EstimationTiantian Zhu, Man Lan. 265-270 [doi]
• ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual EntailmentJiang Zhao, Tiantian Zhu, Man Lan. 271-277 [doi]
• ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical NotesParth Pathak, Pinal Patel, Vishal Panchal, Narayan Choudhary, Amrish Patel, Gautam Joshi. 278-283 [doi]
• FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic SimilarityNgoc Phuoc An Vo, Tommaso Caselli, Octavian Popescu. 284-288 [doi]
• FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTENgoc Phuoc An Vo, Octavian Popescu, Tommaso Caselli. 289-293 [doi]
• GPLSI: Supervised Sentiment Analysis in Twitter using SkipgramsJavi Fernández, Yoan Gutiérrez, José Manuel Gómez Soriano, Patricio Martínez-Barco. 294-299 [doi]
• haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTELorenzo Ferrone, Fabio Massimo Zanzotto. 300-304 [doi]
• HulTech: A General Purpose System for Cross-Level Semantic Similarity based on Anchor Web CountsJose G. Moreno, Rumen Moraliyski, Asma Berrezoug, Gaël Dias. 305-308 [doi]
• IHS R&D Belarus: Cross-domain extraction of product features using CRFMaryna Chernyshevich. 309-313 [doi]
• IITP: A Supervised Approach for Disorder Mention Detection and DisambiguationUtpal Kumar Sikdar, Asif Ekbal, Sriparna Saha 0001. 314-318 [doi]
• IITP: Supervised Machine Learning for Aspect based Sentiment AnalysisDeepak Kumar Gupta, Asif Ekbal. 319-323 [doi]
• IITPatna: Supervised Approach for Sentiment Analysis in TwitterRaja Selvarajan, Asif Ekbal. 324-328 [doi]
• Illinois-LH: A Denotational and Distributional Approach to SemanticsAlice Lai, Julia Hockenmaier. 329-334 [doi]
• In-House: An Ensemble of Pre-Existing Off-the-Shelf ParsersYusuke Miyao, Stephan Oepen, Daniel Zeman. 335-340 [doi]
• Indian Institute of Technology-Patna: Sentiment Analysis in TwitterVikram Singh, Arif Md. Khan, Asif Ekbal. 341-345 [doi]
• INSIGHT Galway: Syntactic and Lexical Features for Aspect Based Sentiment AnalysisSapna Negi, Paul Buitelaar. 346-350 [doi]
• iTac: Aspect Based Sentiment Analysis using Sentiment Trees and DictionariesFritjof Bornebusch, Glaucia Cancino, Melanie Diepenbeck, Rolf Drechsler, Smith Djomkam, Alvine Nzeungang Fanseu, Maryam Jalali, Marc Michael, Jamal Mohsen, Max Nitze, Christina Plump, Mathias Soeken, Hubert Fred Tchambo, Toni, Henning Ziegler. 351-355 [doi]
• IUCL: Combining Information Sources for SemEval Task 5Alex Rudnick, Levi King, Can Liu, Markus Dickinson, Sandra Kübler. 356-360 [doi]
• IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical TextsKoldo Gojenola, Maite Oronoz, Alicia Pérez, Arantza Casillas. 361-365 [doi]
• JOINT_FORCES: Unite Competing Sentiment Classifiers with Random ForestOliver Dürr, Fatih Uzdilli, Mark Cieliebak. 366-369 [doi]
• JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment AnalysisBraja Gopal Patra, Soumik Mandal, Dipankar Das, Sivaji Bandyopadhyay. 370-374 [doi]
• JU-Evora: A Graph Based Cross-Level Semantic Similarity Analysis using Discourse InformationSwarnendu Ghosh, Nibaran Das, Teresa Gonçalves, Paulo Quaresma. 375-379 [doi]
• Kea: Sentiment Analysis of Phrases Within Short TextsAmeeta Agrawal, Aijun An. 380-384 [doi]
• KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial ContextWillem Mattelaer, Mathias Verbeke, Davide Nitti. 385-390 [doi]
• KUNLPLab: Sentiment Analysis on Twitter DataBeakal Gizachew Assefa. 391-394 [doi]
• Linköping: Cubic-Time Graph Parsing with a Simple Scoring SchemeMarco Kuhlmann. 395-399 [doi]
• LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficientDavide Buscaldi, Jorge García Flores, Joseph Le Roux, Nadi Tomeh, Belém Priego Sanchez. 400-405 [doi]
• LT3: Sentiment Classification in User-Generated Content Using a Rich Feature SetCynthia Van Hee, Marjan Van de Kauter, Orphée De Clercq, Els Lefever, Véronique Hoste. 406-410 [doi]
• LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to EnglishDavid Vilares, Miguel Hermo, Miguel A. Alonso, Carlos Gómez-Rodríguez, Yerai Doval. 411-415 [doi]
• Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity SystemsAbhay L. Kashyap, Lushan Han, Roberto Yus, Jennifer Sleeman, Taneeya Satyapanich, Sunil Gandhi, Tim Finin. 416-423 [doi]
• MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7Alejandro Riveros, Maria De-Arteaga, Fabio A. González, Sergio Jiménez 0001, Henning Müller. 424-427 [doi]
• NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter MessagesPedro Paulo Balage Filho, Lucas Avanço, Thiago Alexandre Salgueiro Pardo, Maria das Graças Volpe Nunes. 428-432 [doi]
• NILC_USP: Aspect Extraction using Semantic LabelsPedro Balage Filho, Thiago Pardo. 433-436 [doi]
• NRC-Canada-2014: Detecting Aspects and Sentiment in Customer ReviewsSvetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, Saif Mohammad. 437-442 [doi]
• NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of TweetsXiaodan Zhu, Svetlana Kiritchenko, Saif Mohammad. 443-447 [doi]
• NTNU: Measuring Semantic Similarity with Sublexical Feature Representations and Soft CardinalityAndré Lynum, Partha Pakray, Björn Gambäck, Sergio Jiménez 0001. 448-453 [doi]
• OPI: Semeval-2014 Task 3 System DescriptionMarek Kozlowski. 454-458 [doi]
• Peking: Profiling Syntactic Tree Parsing Techniques for Semantic Graph ParsingYantao Du, Fan Zhang, Weiwei Sun, Xiaojun Wan. 459-464 [doi]
• Potsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic ParsingZeljko Agic, Alexander Koller. 465-470 [doi]
• Priberam: A Turbo Semantic Parser with Second Order FeaturesAndré F. T. Martins, Mariana S. C. Almeida. 471-476 [doi]
• RelAgent: Entity Detection and Normalization for Diseases in Clinical Records: a Linguistically Driven ApproachS. V. Ramanan, P. Senthil Nathan. 477-481 [doi]
• RoBox: CCG with Structured Perceptron for Supervised Semantic Parsing of Robotic Spatial CommandsKilian Evang, Johan Bos. 482-486 [doi]
• RTM-DCU: Referential Translation Machines for Semantic SimilarityErgun Biçici, Andy Way. 487-496 [doi]
• RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short MessagesTobias Günther, Jean Vancoppenolle, Richard Johansson. 497-502 [doi]
• SA-UZH: Verb-based Sentiment AnalysisNora Hollenstein, Michael Amsler, Martina Bachmann, Manfred Klenner. 503-507 [doi]
• SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule SimilarityKalliopi Zervanou, Nikolaos Malandrakis, Shrikanth Narayanan. 508-511 [doi]
• SAIL: Sentiment Analysis using Semantic Similarity and Contrast FeaturesNikolaos Malandrakis, Michael Falcone, Colin Vaz, Jesse James Bisogni, Alexandros Potamianos, Shrikanth Narayanan. 512-516 [doi]
• SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment AnalysisNaveen Nandan, Daniel Dahlmeier, Akriti Vij, Nishtha Malhotra. 517-521 [doi]
• SAP-RI: Twitter Sentiment Analysis in Two DaysAkriti Vij, Nishtha Malhotra, Naveen Nandan, Daniel Dahlmeier. 522-526 [doi]
• SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment AnalysisPengfei Liu, Helen M. Meng. 527-531 [doi]
• SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight MatchingThomas Proisl, Stefan Evert, Paul Greiner, Besim Kabashi. 532-540 [doi]
• Sensible: L2 Translation Assistance by Emulating the Manual Post-Editing ProcessLiling Tan, Anne Schumann, José Manuel Martínez Martínez, Francis Bond. 541-545 [doi]
• Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9José Saias. 546-550 [doi]
• SentiKLUE: Updating a Polarity Classifier in 48 HoursStefan Evert, Thomas Proisl, Paul Greiner, Besim Kabashi. 551-555 [doi]
• ShrdLite: Semantic Parsing Using a Handmade GrammarPeter Ljunglöf. 556-559 [doi]
• SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level SimilarityCarmen Banea, Di Chen, Rada Mihalcea, Claire Cardie, Janyce Wiebe. 560-565 [doi]
• SINAI: Voting System for Aspect Based Sentiment AnalysisSalud María Jiménez Zafra, Eugenio Martínez-Cámara, Maite Martín, Luis Alfonso Ureña López. 566-571 [doi]
• SINAI: Voting System for Twitter Sentiment AnalysisEugenio Martínez-Cámara, Salud María Jiménez Zafra, Maite Martín, Luis Alfonso Ureña López. 572-577 [doi]
• SNAP: A Multi-Stage XML-Pipeline for Aspect Based Sentiment AnalysisClemens Schulze Wettendorf, Robin Jegan, Allan Körner, Julia Zerche, Nataliia Plotnikova, Julian Moreth, Tamara Schertl, Verena Obermeyer, Susanne Streil, Tamara Willacker, Stefan Evert. 578-584 [doi]
• SSMT: A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic SimilarityPingping Huang, Baobao Chang. 585-589 [doi]
• SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in TwitterBoris Velichkov, Borislav Kapukaranov, Ivan Grozev, Jeni Karanesheva, Todor Mihaylov, Yasen Kiprov, Preslav Nakov, Ivan Koychev, Georgi Georgiev. 590-595 [doi]
• Supervised Methods for Aspect-Based Sentiment AnalysisHussam Hamdan, Patrice Bellot, Frédéric Béchet. 596-600 [doi]
• Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-GramsMartin Jaggi, Fatih Uzdilli, Mark Cieliebak. 601-604 [doi]
• Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment AnalysisAlexandre Denis, Samuel Cruz-Lara, Nadia Bellalem, Lotfi Bellalem. 605-609 [doi]
• SZTE-NLP: Aspect level opinion mining exploiting syntactic cuesViktor Hangya, Gábor Berend, István Varga, Richárd Farkas. 610-614 [doi]
• SZTE-NLP: Clinical Text Analysis with Named Entity RecognitionMelinda Katona, Richárd Farkas. 615-618 [doi]
• TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR ApproachArun Kumar Jayapal, Martin Emms, John D. Kelleher. 619-623 [doi]
• Team Z: Wiktionary as a L2 Writing AssistantAnubhav Gupta. 624-627 [doi]
• TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced DataYasuhide Miura, Shigeyuki Sakaki, Keigo Hattori, Tomoko Ohkuma. 628-632 [doi]
• TeamZ: Measuring Semantic Textual Similarity for Spanish Using an Overlap-Based ApproachAnubhav Gupta. 633-635 [doi]
• The Impact of Z_score on Twitter Sentiment AnalysisHussam Hamdan, Patrice Bellot, Frédéric Béchet. 636-641 [doi]
• The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic SimilarityJohannes Bjerva, Johan Bos, Rob van der Goot, Malvina Nissim. 642-646 [doi]
• Think Positive: Towards Twitter Sentiment Analysis from ScratchCícero Nogueira dos Santos. 647-651 [doi]
• ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional SemanticsAnkur Parikh, P. V. S. Avinesh, Joy Mustafi, Lalit Agarwalla, Ashish Mungi. 652-656 [doi]
• TJP: Identifying the Polarity of Tweets from ContextsTawunrat Chalothorn, Jeremy Ellman. 657-662 [doi]
• TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7Jitendra Jonnagaddala, Manish Kumar, Hong-Jie Dai, Enny Rachmani, Chien-Yeh Hsu. 663-667 [doi]
• tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate SelectionArodami Chorianopoulou, Georgia Athanasopoulou, Elias Iosif, Ioannis Klasinas, Alexandros Potamianos. 668-672 [doi]
• TUGAS: Exploiting unlabelled data for Twitter sentiment analysisSilvio Amir, Miguel B. Almeida, Bruno Martins, João Filgueiras, Mário J. Silva. 673-677 [doi]
• Turku: Broad-Coverage Semantic Parsing with Rich FeaturesJenna Kanerva, Juhani Luotolahti, Filip Ginter. 678-682 [doi]
• UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment AnalysisViktor Pekar, Naveed Afzal, Bernd Bohnet. 683-687 [doi]
• UEdin: Translating L1 Phrases in L2 Context using Context-Sensitive SMTEva Hasler. 688-693 [doi]
• ÚFAL: Using Hand-crafted Rules in Aspect Based Sentiment Analysis on Parsed DataKaterina Veselovská, Ales Tamchyna. 694-698 [doi]
• UIO-Lien: Entailment Recognition using Minimal Recursion SemanticsElisabeth Lien, Milen Kouylekov. 699-703 [doi]
• UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter DataLucie Flekova, Oliver Ferschke, Iryna Gurevych. 704-710 [doi]
• ULisboa: Identification and Classification of Medical ConceptsAndré Leal, Diogo Gonçalves, Bruno Martins, Francisco M. Couto. 711-715 [doi]
• UMCC_DLSI_SemSim: Multilingual System for Measuring Semantic Textual SimilarityAlexander Chavez, Héctor Dávila, Yoan Gutiérrez, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz. 716-721 [doi]
• UMCC_DLSI: A Probabilistic Automata for Aspect Based Sentiment AnalysisYenier Castañeda, Armando Collazo, Elvis Crego, Jorge L. Garcia, Yoan Gutiérrez, David Tomás, Andrés Montoyo, Rafael Muñoz. 722-726 [doi]
• UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet SimilarityPedro Aniel Sánchez-Mirabal, Yarelis Ruano Torres, Suilen Hernández Alvarado, Yoan Gutiérrez, Andrés Montoyo, Rafael Muñoz. 727-731 [doi]
• UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and EntailmentSergio Jiménez 0001, George Dueñas, Julia Baquero, Alexander F. Gelbukh. 732-742 [doi]
• UNAL-NLP: Cross-Lingual Phrase Sense Disambiguation with Syntactic Dependency TreesEmilio Silva-Schlenker, Sergio Jiménez 0001, Julia Baquero. 743-747 [doi]
• UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual SimilarityPierpaolo Basile, Annalina Caputo, Giovanni Semeraro. 748-753 [doi]
• UniPi: Recognition of Mentions of Disorders in Clinical TextGiuseppe Attardi, Vittoria Cozza, Daniele Sartiano. 754-760 [doi]
• UNITOR: Aspect Based Sentiment Analysis with Structured LearningGiuseppe Castellucci, Simone Filice, Danilo Croce, Roberto Basili. 761-767 [doi]
• University_of_Warwick: SENTIADAPTRON - A Domain Adaptable Sentiment Analyser for Tweets - Meets SemEvalRichard Townsend, Aaron Kalair, Ojas Kulkarni, Rob Procter, Maria Liakata. 768-772 [doi]
• UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment ResourceReynier Ortega Bueno, Adrian Fonseca-Bruzón, Carlos Muñiz Cuza, Yoan Gutiérrez, Andrés Montoyo. 773-778 [doi]
• UoW: Multi-task Learning Gaussian Process for Semantic Textual SimilarityMiguel Rios. 779-784 [doi]
• UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual EntailmentRohit Gupta, Hanna Béchara, Ismaïl El Maarouf, Constantin Orasan. 785-789 [doi]
• USF: Chunking for Aspect-term Identification & Polarity ClassificationCindi Thompson. 790-795 [doi]
• UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic LogicIslam Beltagy, Stephen Roller, Gemma Boleda, Katrin Erk, Raymond J. Mooney. 796-801 [doi]
• UTH_CCB: A report for SemEval 2014 - Task 7 Analysis of Clinical TextYaoyun Zhang, Jingqi Wang, Buzhou Tang, Yonghui Wu, Min Jiang, Yukun Chen, Hua Xu. 802-806 [doi]
• UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space RepresentationsSuwisa Kaewphan, Kai Hakala, Filip Ginter. 807-811 [doi]
• UW-MRS: Leveraging a Deep Grammar for Robotic Spatial CommandsWoodley Packard. 812-816 [doi]
• UWB: Machine Learning Approach to Aspect-Based Sentiment AnalysisTomás Brychcín, Michal Konkol, Josef Steinberger. 817-822 [doi]
• UWM: Applying an Existing Trainable Semantic Parser to Parse Robotic Spatial CommandsRohit J. Kate. 823-827 [doi]
• UWM: Disorder Mention Extraction from Clinical Text Using CRFs and Normalization Using Learned Edit Distance PatternsOmid Ghiasvand, Rohit J. Kate. 828-832 [doi]
• V3: Unsupervised Generation of Domain Aspect Terms for Aspect Based Sentiment AnalysisAitor García Pablos, Montse Cuadros, German Rigau. 833-837 [doi]
• XRCE: Hybrid Classification for Aspect-based Sentiment AnalysisCaroline Brun, Diana Nicoleta Popa, Claude Roux. 838-842 [doi]