Table of Contents

• Front Matter [doi]
• SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)Wei Xu, Chris Callison-Burch, Bill Dolan. 1-11 [doi]
• MITRE: Seven Systems for Semantic Similarity in TweetsGuido Zarrella, John C. Henderson, Elizabeth M. Merkhofer, Laura Strickhart. 12-17 [doi]
• CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering TaskHelena Gómez-Adorno, Darnes Vilariño, David Pinto, Grigori Sidorov. 18-22 [doi]
• HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity FeaturesDario Bertero, Pascale Fung. 23-28 [doi]
• FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in TwitterNgoc Phuoc An Vo, Simone Magnolini, Octavian Popescu. 29-33 [doi]
• ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in TwitterJiang Zhao, Man Lan. 34-39 [doi]
• ROB: Using Semantic Meaning to Recognize ParaphrasesRob van der Goot, Gertjan van Noord. 40-44 [doi]
• AMRITA_CEN$@$SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive AutoencodersMahalakshmi Shanumuga Sundaram, Anand Kumar Madasamy, Soman Kotti Padannayil. 45-50 [doi]
• Ebiquity: Paraphrase and Semantic Similarity in Twitter using SkipgramsTaneeya Satyapanich, Hang Gao, Tim Finin. 51-55 [doi]
• RTM-DCU: Predicting Semantic Similarity with Referential Translation MachinesErgun Biçici. 56-63 [doi]
• Twitter Paraphrase Identification with Simple Overlap Features and SVMsAsli Eyecioglu, Bill Keller. 64-69 [doi]
• TKLBLIIR: Detecting Twitter Paraphrases with TweetingJayMladen Karan, Goran Glavas, Jan Snajder, Bojana Dalbelo Basic, Ivan Vulic, Marie-Francine Moens. 70-74 [doi]
• CDTDS: Predicting Paraphrases in Twitter via Support Vector RegressionRafael-Michael Karampatsis. 75-79 [doi]
• yiGou: A Semantic Text Similarity Computing System Based on SVMYang Liu, Chengjie Sun, Lei Lin, Xiaolong Wang. 80-84 [doi]
• USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation MetricsLiling Tan, Carolina Scarton, Lucia Specia, Josef van Genabith. 85-89 [doi]
• TrWP: Text Relatedness using Word and Phrase RelatednessMd. Rashadul Hasan Rakib, Aminul Islam, Evangelos E. Milios. 90-95 [doi]
• MiniExperts: An SVM Approach for Measuring Semantic Textual SimilarityHanna Béchara, Hernani Costa, Shiva Taslimipoor, Rohit Gupta, Constantin Orasan, Gloria Corpas Pastor, Ruslan Mitkov. 96-101 [doi]
• FBK-HLT: A New Framework for Semantic Textual SimilarityNgoc Phuoc An Vo, Simone Magnolini, Octavian Popescu. 102-106 [doi]
• UMDuluth-BlueTeam: SVCSTS - A Multilingual and Chunk Level Semantic Similarity SystemSakethram Karumuri, Viswanadh Kumar Reddy Vuggumudi, Sai Charan Raj Chitirala. 107-110 [doi]
• SemantiKLUE: Semantic Textual Similarity with Maximum Weight MatchingNataliia Plotnikova, Gabriella Lapesa, Thomas Proisl, Stefan Evert. 111-116 [doi]
• ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity EstimationJiang Zhao, Man Lan, Junfeng Tian. 117-122 [doi]
• UQeResearch: Semantic Textual Similarity QuantificationHamed Hassanzadeh, Tudor Groza, Anthony N. Nguyen, Jane Hunter. 123-127 [doi]
• WSL: Sentence Similarity Using Semantic Distance Between WordsNaoko Miura, Tomohiro Takagi. 128-131 [doi]
• SOPA: Random Forests Regression for the Semantic Textual Similarity taskDavide Buscaldi, Jorge García Flores, Iván V. Meza, Isaac Rodriguez. 132-137 [doi]
• MathLingBudapest: Concept Networks for Semantic SimilarityGábor Recski, Judit Ács. 138-142 [doi]
• DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2Piyush Arora, Chris Hokamp, Jennifer Foster, Gareth J. F. Jones. 143-147 [doi]
• DLS$@$CU: Sentence Similarity from Word Alignment and Semantic Vector CompositionMd. Arafat Sultan, Steven Bethard, Tamara Sumner. 148-153 [doi]
• FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual SimilarityBasma Hassan, Samir AbdelRahman, Reem Bahgat. 154-158 [doi]
• AZMAT: Sentence Similarity Using Associative MatricesEvan Jaffe, Lifeng Jin, David King, Marten Van Schijndel. 159-163 [doi]
• NeRoSim: A System for Measuring and Interpreting Semantic Textual SimilarityRajendra Banjade, Nobal Bikram Niraula, Nabin Maharjan, Vasile Rus, Dan Stefanescu, Mihai C. Lintean, Dipesh Gautam. 164-171 [doi]
• Samsung: Align-and-Differentiate Approach to Semantic Textual SimilarityLushan Han, Justin Martineau, Doreen Cheng, Christopher Thomas. 172-177 [doi]
• UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STSEneko Agirre, Aitor Gonzalez-Agirre, Iñigo Lopez-Gazpio, Montse Maritxalar, German Rigau, Larraitz Uria. 178-183 [doi]
• ASAP-II: From the Alignment of Phrases to Textual SimilarityAna Oliveira Alves, David Simões, Hugo Gonçalo Oliveira, Adriana Ferrugento. 184-189 [doi]
• TATO: Leveraging on Multiple Strategies for Semantic Textual SimilarityTu Thanh Vu, Quan Hung Tran, Son Bao Pham. 190-195 [doi]
• HITSZ-ICRC: Exploiting Classification Approach for Answer Selection in Community Question AnsweringYongshuai Hou, Cong Tan, Xiaolong Wang, Yaoyun Zhang, Jun Xu 0007, Qingcai Chen. 196-202 [doi]
• QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and EnglishMassimo Nicosia, Simone Filice, Alberto Barrón-Cedeño, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluís Màrquez, Shafiq R. Joty, Walid Magdy. 203-209 [doi]
• ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection ChallengeXiaoqiang Zhou, Baotian Hu, Jiaxin Lin, Yang Xiang, Xiaolong Wang. 210-214 [doi]
• JAIST: Combining multiple features for Answer Selection in Community Question AnsweringQuan Hung Tran, Vu Tran, Tu Vu, Minh Nguyen, Son Bao Pham. 215-219 [doi]
• Shiraz: A Proposed List Wise Approach to Answer ValidationAmin Heydari Alashty, Saeed Rahmani, Meysam Roostaee, Mostafa Fakhrahmad. 220-225 [doi]
• Al-Bayan: A Knowledge-based System for Arabic Answer SelectionReham Mohamed, Maha Ragab, Heba Abdelnasser, Nagwa M. El-Makky, Marwan Torki. 226-230 [doi]
• FBK-HLT: An Application of Semantic Textual Similarity for Answer Selection in Community Question AnsweringNgoc Phuoc An Vo, Simone Magnolini, Octavian Popescu. 231-235 [doi]
• ECNU: Using Multiple Sources of CQA-based Information for Answers Selection and YES/NO Response InferenceLiang Yi, Jianxiang Wang, Man Lan. 236-241 [doi]
• Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance FeaturesIvan Zamanov, Marina Kraeva, Nelly Hateva, Ivana Yovcheva, Ivelina Nikolova, Galia Angelova. 242-246 [doi]
• CoMiC: Adapting a Short Answer Assessment System for Answer SelectionBjörn Rudzewitz, Ramon Ziai. 247-251 [doi]
• SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on InterpretabilityEneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, Janyce Wiebe. 252-263 [doi]
• ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual SimilarityChristian Hänig, Robert Remus, Xose de la Puente. 264-268 [doi]
• SemEval-2015 Task 3: Answer Selection in Community Question AnsweringPreslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, Jim Glass, Bilal Randeree. 269-281 [doi]
• VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering SystemsYonatan Belinkov, Mitra Mohtarami, Scott Cyphers, James Glass. 282-287 [doi]
• SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity LinkingAndrea Moro 0001, Roberto Navigli. 288-297 [doi]
• LIMSI: Translations as Source of Indirect Supervision for Multilingual All-Words Sense Disambiguation and Entity LinkingMarianna Apidianaki, Li Gong. 298-302 [doi]
• SemEval-2015 Task 14: Analysis of Clinical TextNoémie Elhadad, Sameer Pradhan, Sharon Lipsky Gorman, Suresh Manandhar, Wendy W. Chapman, Guergana K. Savova. 303-310 [doi]
• UTH-CCB: The Participation of the SemEval 2015 Challenge - Task 14Jun Xu 0007, Yaoyun Zhang, Jingqi Wang, Yonghui Wu, Min Jiang, Ergin Soysal, Hua Xu. 311-314 [doi]
• SemEval-2015 Task 15: A CPA dictionary-entry-building taskVít Baisa, Jane Bradbury, Silvie Cinková, Ismaïl El Maarouf, Adam Kilgarriff, Octavian Popescu. 315-324 [doi]
• BLCUNLP: Corpus Pattern Analysis for Verbs Based on Dependency ChainYukun Feng, Qiao Deng, Dong Yu. 325-328 [doi]
• WSD-games: a Game-Theoretic Algorithm for Unsupervised Word Sense DisambiguationRocco Tripodi, Marcello Pelillo. 329-334 [doi]
• DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense DisambiguationDirk Weissenborn, Feiyu Xu, Hans Uszkoreit. 335-339 [doi]
• EBL-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based TechniqueEniafe Festus Ayetiran, Guido Boella. 340-344 [doi]
• VUA-background : When to Use Background Information to Perform Word Sense DisambiguationMarten Postma, Rubén Izquierdo, Piek Vossen. 345-349 [doi]
• TeamUFAL: WSD+EL as Document RetrievalPetr Fanta, Roman Sudarikov, Ondrej Bojar. 350-354 [doi]
• EL92: Entity Linking Combining Open Source Annotators via Weighted VotingPablo Ruiz 0001, Thierry Poibeau. 355-359 [doi]
• UNIBA: Combining Distributional Semantic Models and Sense Distribution for Multilingual All-Words Sense Disambiguation and Entity LinkingPierpaolo Basile, Annalina Caputo, Giovanni Semeraro. 360-364 [doi]
• SUDOKU: Treating Word Sense Disambiguation & Entitiy Linking as a Deterministic Problem - via an Unsupervised & Iterative ApproachSteve L. Manion. 365-369 [doi]
• TeamHCMUS: Analysis of Clinical TextNghia Huynh, Quoc Ho. 370-374 [doi]
• UTU: Adapting Biomedical Event Extraction System to Disorder Attribute DetectionKai Hakala. 375-379 [doi]
• IHS-RD-Belarus: Identification and Normalization of Disorder Concepts in Clinical NotesMaryna Chernyshevich, Vadim Stankevitch. 380-384 [doi]
• UWM: A Simple Baseline Method for Identifying Attributes of Disease and Disorder Mentions in Clinical TextOmid Ghiasvand, Rohit J. Kate. 385-388 [doi]
• TAKELAB: Medical Information Extraction and Linking with MINERALGoran Glavas. 389-393 [doi]
• TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical NotesJitendra Jonnagaddala, Siaw-Teng Liaw, Pradeep Kumar Ray, Manish Kumar, Hong-Jie Dai. 394-398 [doi]
• UtahPOET: Disorder Mention Identification and Context Slot Filling with Cognitive InspirationKristina Doing-Harris, Sean Igo, Jianlin Shi, John F. Hurdle. 399-405 [doi]
• ULisboa: Recognition and Normalization of Medical ConceptsAndré Leal, Bruno Martins, Francisco M. Couto. 406-411 [doi]
• ezDI: A Supervised NLP System for Clinical Narrative AnalysisParth Pathak, Pinal Patel, Vishal Panchal, Sagar Soni, Kinjal Dani, Amrish Patel, Narayan Choudhary. 412-416 [doi]
• CUAB: Supervised Learning of Disorders and their Attributes using RelationsJames Gung, John Osborne, Steven Bethard. 417-421 [doi]
• BioinformaticsUA: Machine Learning and Rule-Based Recognition of Disorders and Clinical Attributes from Patient NotesSérgio Matos, José Sequeira, José Luís Oliveira. 422-426 [doi]
• LIST-LUX: Disorder Identification from Clinical TextsAsma Ben Abacha, Aikaterini Karanasiou, Yassine Mrabet, Júlio Cesar dos Reis. 427-432 [doi]
• CMILLS: Adapting Semantic Role Labeling Features to Dependency ParsingChad Mills, Gina-Anne Levow. 433-437 [doi]
• Duluth: Word Sense Discrimination in the Service of LexicographyTed Pedersen. 438-442 [doi]
• SemEval-2015 Task 9: CLIPEval Implicit Polarity of EventsIrene Russo, Tommaso Caselli, Carlo Strapparava. 443-450 [doi]
• SemEval-2015 Task 10: Sentiment Analysis in TwitterSara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif Mohammad, Alan Ritter, Veselin Stoyanov. 451-463 [doi]
• UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment ClassificationAliaksei Severyn, Alessandro Moschitti. 464-469 [doi]
• SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in TwitterAniruddha Ghosh, Guofu Li, Tony Veale, Paolo Rosso, Ekaterina Shutova, John A. Barnden, Antonio Reyes. 470-478 [doi]
• CLaC-SentiPipe: SemEval2015 Subtasks 10 B, E, and Task 11Canberk Özdemir, Sabine Bergler. 479-485 [doi]
• SemEval-2015 Task 12: Aspect Based Sentiment AnalysisMaria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, Ion Androutsopoulos. 486-495 [doi]
• NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target ExtractionZhiqiang Toh, Jian Su. 496-501 [doi]
• SHELLFBK: An Information Retrieval-based System For Multi-Domain Sentiment AnalysisMauro Dragoni. 502-509 [doi]
• DIEGOLab: An Approach for Message-level Sentiment Classification in TwitterAbeed Sarker, Azadeh Nikfarjam, Davy Weissenbacher, Graciela Gonzalez. 510-514 [doi]
• Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of TweetsLi Dong, Furu Wei, Yichun Yin, Ming Zhou, Ke Xu. 515-519 [doi]
• IIIT-H at SemEval 2015: Twitter Sentiment Analysis - The Good, the Bad and the Neutral!Ayushi Dalmia, Manish Gupta, Vasudeva Varma. 520-526 [doi]
• CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in TwitterSebastian Ebert, Ngoc Thang Vu, Hinrich Schütze. 527-532 [doi]
• GTI: An Unsupervised Approach for Sentiment Analysis in TwitterMilagros Fernández Gavilanes, Tamara Álvarez-López, Jonathan Juncal-Martínez, Enrique Costa-Montenegro, Francisco Javier González Castaño. 533-538 [doi]
• Gradiant-Analytics: Training Polarity Shifters with CRFs for Message Level Polarity DetectionHéctor Cerezo-Costas, Diego Celix-Salgado. 539-544 [doi]
• IOA: Improving SVM Based Sentiment Classification Through Post ProcessingPeijia Li, Weiqun Xu, Chenglong Ma, Jia Sun, Yonghong Yan 0002. 545-550 [doi]
• RoseMerry: A Baseline Message-level Sentiment Classification SystemHuizhi Liang, Richard Fothergill, Timothy Baldwin. 551-555 [doi]
• UDLAP: Sentiment Analysis Using a Graph-Based RepresentationEsteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez, J. Alfredo Sánchez. 556-560 [doi]
• ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding FeaturesZhihua Zhang, GuoShun Wu, Man Lan. 561-567 [doi]
• Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in TwitterHussam Hamdan, Patrice Bellot, Frédéric Béchet. 568-573 [doi]
• ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015Mayte Giménez, Ferran Pla, Lluís F. Hurtado. 574-581 [doi]
• Webis: An Ensemble for Twitter Sentiment DetectionMatthias Hagen, Martin Potthast, Michel Büchner, Benno Stein. 582-589 [doi]
• Sentibase: Sentiment Analysis in Twitter on a BudgetSatarupa Guha, Aditya Joshi, Vasudeva Varma. 590-594 [doi]
• UNIBA: Sentiment Analysis of English Tweets Combining Micro-blogging, Lexicon and Semantic FeaturesPierpaolo Basile, Nicole Novielli. 595-600 [doi]
• IITPSemEval: Sentiment Discovery from 140 CharactersAyush Kumar 0003, Vamsi Krishna, Asif Ekbal. 601-607 [doi]
• Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for SentimentFatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, Mark Cieliebak. 608-612 [doi]
• INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon InductionSilvio Amir, Wang Ling, Ramón Fernández Astudillo, Bruno Martins, Mário J. Silva, Isabel Trancoso. 613-618 [doi]
• KLUEless: Polarity Classification and AssociationNataliia Plotnikova, Micha Kohl, Kevin Volkert, Stefan Evert, Andreas Lerner, Natalie Dykes, Heiko Ermer. 619-625 [doi]
• SWASH: A Naive Bayes Classifier for Tweet Sentiment IdentificationRuth Talbot, Chloe Acheampong, Richard Wicentowski. 626-630 [doi]
• SWATCS65: Sentiment Classification Using an Ensemble of Class ProjectsRichard Wicentowski. 631-635 [doi]
• SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic RegressionYousef Alhessi, Richard Wicentowski. 636-639 [doi]
• TwitterHawk: A Feature Bucket Based Approach to Sentiment AnalysisWilliam Boag, Peter Potash, Anna Rumshisky. 640-646 [doi]
• SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised LearningPrerna Chikersal, Soujanya Poria, Erik Cambria. 647-651 [doi]
• INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding SubspacesRamón Fernández Astudillo, Silvio Amir, Wang Ling, Bruno Martins, Mário J. Silva, Isabel Trancoso. 652-656 [doi]
• WarwickDCS: From Phrase-Based to Target-Specific Sentiment RecognitionRichard Townsend, Adam Tsakalidis, Yiwei Zhou, Bo Wang, Maria Liakata, Arkaitz Zubiaga, Alexandra I. Cristea, Rob Procter. 657-663 [doi]
• UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015Xu Han, Binyang Li, Jing Ma, Yuxiao Zhang, Gaoyan Ou, Tengjiao Wang, Kam-Fai Wong. 664-668 [doi]
• SWAT-CMW: Classification of Twitter Emotional Polarity using a Multiple-Classifier Decision Schema and Enhanced Emotion TaggingRiley Collins, Daniel May, Noah Weinthal, Richard Wicentowski. 669-672 [doi]
• LLT-PolyU: Identifying Sentiment Intensity in Ironic TweetsHongzhi Xu, Enrico Santus, Anna Laszlo, Chu-Ren Huang. 673-678 [doi]
• KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in TwitterNguyen Hoang Long, Duc Nguyen Trung, Dosam Hwang, Jason J. Jung. 679-683 [doi]
• LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReallyCynthia Van Hee, Els Lefever, Véronique Hoste. 684-688 [doi]
• PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11Parth Gupta, Jon Ander Gómez. 689-693 [doi]
• ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and SarcasmDelia Irazú Hernández Farías, Emilio Sulis, Viviana Patti, Giancarlo Ruffo, Cristina Bosco. 694-698 [doi]
• CPH: Sentiment analysis of Figurative Language on Twitter #easypeasy #notSarah McGillion, Héctor Martínez Alonso, Barbara Plank. 699-703 [doi]
• UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in TwitterFrancesco Barbieri, Francesco Ronzano, Horacio Saggion. 704-708 [doi]
• DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on TwitterMaria Karanasou, Christos Doulkeridis, Maria Halkidi. 709-713 [doi]
• V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12Aitor García Pablos, Montse Cuadros, German Rigau. 714-718 [doi]
• LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment AnalysisOrphée De Clercq, Marjan Van de Kauter, Els Lefever, Véronique Hoste. 719-724 [doi]
• UFRGS: Identifying Categories and Targets in Customer ReviewsAnderson Uilian Kauer, Viviane Pereira Moreira. 725-729 [doi]
• SINAI: Syntactic Approach for Aspect-Based Sentiment AnalysisSalud M. Jiménez Zafra, Eugenio Martínez-Cámara, Maria Teresa Martín-Valdivia, Luis Alfonso Ureña López. 730-735 [doi]
• ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in ReviewsZhihua Zhang, Man Lan. 736-741 [doi]
• UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment AnalysisRavikanth Repaka, Ranga Reddy Pallelra, Akshay Reddy Koppula, Venkata Subhash Movva. 742-747 [doi]
• EliXa: A Modular and Flexible ABSA PlatformIñaki San Vicente, Xabier Saralegi, Rodrigo Agerri. 748-752 [doi]
• Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity AnalysisHussam Hamdan, Patrice Bellot, Frédéric Béchet. 753-758 [doi]
• SIEL: Aspect Based Sentiment Analysis in ReviewsSatarupa Guha, Aditya Joshi, Vasudeva Varma. 759-766 [doi]
• Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12José Saias. 767-771 [doi]
• TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity ClassificationZhifei Zhang, Jian-Yun Nie, Hongling Wang. 772-777 [doi]
• SemEval-2015 Task 4: TimeLine: Cross-Document Event OrderingAnne-Lyse Minard, Manuela Speranza, Eneko Agirre, Itziar Aldabe, Marieke van Erp, Bernardo Magnini, German Rigau, Ruben Urizar. 778-786 [doi]
• SPINOZA_VU: An NLP Pipeline for Cross Document TimeLinesTommaso Caselli, Antske Fokkens, Roser Morante, Piek Vossen. 787-791 [doi]
• SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question AnsweringHector Llorens, Nathanael Chambers, Naushad UzZaman, Nasrin Mostafazadeh, James F. Allen, James Pustejovsky. 792-800 [doi]
• HLT-FBK: a Complete Temporal Processing System for QA TempEvalParamita Mirza, Anne-Lyse Minard. 801-805 [doi]
• SemEval-2015 Task 6: Clinical TempEvalSteven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky, Marc Verhagen. 806-814 [doi]
• BluLab: Temporal Information Extraction for the 2015 Clinical TempEval ChallengeSumithra Velupillai, Danielle L. Mowery, Samir E. AbdelRahman, Lee M. Christensen, Wendy W. Chapman. 815-819 [doi]
• GPLSIUA: Combining Temporal Information and Topic Modeling for Cross-Document Event OrderingBorja Navarro, Estela Saquete. 820-824 [doi]
• HeidelToul: A Baseline Approach for Cross-document Event OrderingBilel Moulahi, Jannik Strötgen, Michael Gertz, Lynda Tamine. 825-829 [doi]
• HITSZ-ICRC: An Integration Approach for QA TempEval ChallengeYongshuai Hou, Cong Tan, Qingcai Chen, Xiaolong Wang. 830-834 [doi]
• UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEvalHegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski, Marcos Didonet Del Fabro. 835-839 [doi]
• IXAGroupEHUDiac: A Multiple Approach System towards the Diachronic Evaluation of TextsHaritz Salaberri, Iker Salaberri, Olatz Arregi, Beñat Zapirain. 840-845 [doi]
• USAAR-CHRONOS: Crawling the Web for Temporal AnnotationsLiling Tan, Noam Ordan. 846-850 [doi]
• AMBRA: A Ranking Approach to Temporal Text ClassificationMarcos Zampieri, Alina Maria Ciobanu, Vlad Niculae, Liviu P. Dinu. 851-855 [doi]
• IXAGroupEHUSpaceEval: (X-Space) A WordNet-based approach towards the Automatic Recognition of Spatial Information following the ISO-Space Annotation SchemeHaritz Salaberri, Olatz Arregi, Beñat Zapirain. 856-861 [doi]
• UTD: Ensemble-Based Spatial Relation ExtractionJennifer D'Souza, Vincent Ng. 862-869 [doi]
• SemEval 2015, Task 7: Diachronic Text EvaluationOctavian Popescu, Carlo Strapparava. 870-878 [doi]
• UCD : Diachronic Text Classification with Character, Word, and Syntactic N-gramsTerrence Szymanski, Gerard Lynch. 879-883 [doi]
• SemEval-2015 Task 8: SpaceEvalJames Pustejovsky, Parisa KordJamshidi, Marie-Francine Moens, Aaron Levine, Seth Dworman, Zachary Yocum. 884-894 [doi]
• SpRL-CWW: Spatial Relation Classification with Independent Multi-class ModelsEric Nichols, Fadi Botros. 895-901 [doi]
• SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)Georgeta Bordea, Paul Buitelaar, Stefano Faralli, Roberto Navigli. 902-910 [doi]
• INRIASAC: Simple Hypernym Extraction MethodsGregory Grefenstette. 911-914 [doi]
• SemEval 2015 Task 18: Broad-Coverage Semantic Dependency ParsingStephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinková, Dan Flickinger, Jan Hajic, Zdenka Uresová. 915-926 [doi]
• Peking: Building Semantic Dependency Graphs with a Hybrid ParserYantao Du, Fan Zhang, Xun Zhang, Weiwei Sun, Xiaojun Wan. 927-931 [doi]
• USAAR-WLV: Hypernym Generation with Deep Neural NetsLiling Tan, Rohit Gupta, Josef van Genabith. 932-937 [doi]
• NTNU: An Unsupervised Knowledge Approach for Taxonomy ExtractionBamfa Ceesay, Wen-Juan Hou. 938-943 [doi]
• LT3: A Multi-modular Approach to Automatic Taxonomy ConstructionEls Lefever. 944-948 [doi]
• TALN-UPF: Taxonomy Learning Exploiting CRF-Based Hypernym Extraction on Encyclopedic DefinitionsLuis Espinosa Anke, Horacio Saggion, Francesco Ronzano. 949-954 [doi]
• QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw TextsGuillaume Cleuziou, Davide Buscaldi, Gaël Dias, Vincent Levorato, Christine Largeron. 955-959 [doi]
• Riga: from FrameNet to Semantic Frames with C6.0 RulesGuntis Barzdins, Peteris Paikens, Didzis Gosko. 960-964 [doi]
• Turku: Semantic Dependency Parsing as a Sequence ClassificationJenna Kanerva, Juhani Luotolahti, Filip Ginter. 965-969 [doi]
• Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain DataMariana S. C. Almeida, André F. T. Martins. 970-973 [doi]