{ "paper_id": "S15-2000", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T15:37:35.581519Z" }, "title": "Most task organisers and task participants also served as reviewers for the workshop.", "authors": [ { "first": "Samir", "middle": [], "last": "Abdelrahman", "suffix": "", "affiliation": {}, "email": "" }, { "first": "John", "middle": [], "last": "Barnden", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Sabine", "middle": [], "last": "Bergler", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Daniel", "middle": [], "last": "Cer", "suffix": "", "affiliation": {}, "email": "" }, { "first": "", "middle": [], "last": "Google", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Jennifer", "middle": [ "D '" ], "last": "Souza", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Anderson", "middle": [], "last": "Kauer", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Simone", "middle": [], "last": "Magnolini", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Bruno", "middle": [], "last": "Kessler", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Steve", "middle": [ "L" ], "last": "Manion", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Juan", "middle": [ "Miguel" ], "last": "Cejuela", "suffix": "", "affiliation": {}, "email": "" }, { "first": "", "middle": [], "last": "Rostlab", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Anne-Lyse", "middle": [], "last": "Minard", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Naoko", "middle": [], "last": "Miura", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Marie-Francine", "middle": [], "last": "Moens", "suffix": "", "affiliation": {}, "email": "" }, { "first": "K", "middle": [ "U" ], "last": "Leuven", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Reham", "middle": [], "last": "Mohamed", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Andrea", "middle": [], "last": "Moro", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Vivi", "middle": [], "last": "Nastase", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Marco", "middle": [], "last": "Pennacchiotti", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Mohammad", "middle": [ "Taher" ], "last": "Pilehvar", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Athena", "middle": [ "R C Octavian" ], "last": "Popescu", "suffix": "", "affiliation": {}, "email": "" }, { "first": "James", "middle": [], "last": "Pustejovsky", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Veselin", "middle": [], "last": "Stoyanov", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Carlo", "middle": [], "last": "Facebook", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Fbk-Irst", "middle": [ "Jannik" ], "last": "Strapparava", "suffix": "", "affiliation": {}, "email": "" }, { "first": "", "middle": [], "last": "Str\u00f6tgen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Ngoc", "middle": [], "last": "Phuoc", "suffix": "", "affiliation": {}, "email": "" }, { "first": "An", "middle": [], "last": "Vo", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Trento", "middle": [], "last": "Tu", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Thanh", "middle": [], "last": "Vu", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Viswanadh", "middle": [], "last": "Kumar", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Reddy", "middle": [], "last": "Vuggumudi", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Marco", "middle": [], "last": "Baroni", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "S15-2000", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "Welcome to The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyse diverse semantic phenomena in text with the aim of extending the current state of the art in semantic analysis and creating high quality annotated datasets in a range of increasingly challenging problems in natural language semantics. SemEval provides an exciting forum for researchers to propose challenging research problems in semantics and to build systems/techniques to address such research problems.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "SemEval-2015 is the ninth workshop in the series of International Workshops on Semantic Evaluation Exercises. The first three workshops, SensEval-1 (1998), SensEval-2 (2001), and SensEval-3 (2004) , focused on word sense disambiguation, each time growing in the number of languages offered, in the number of tasks, and also in the number of participating teams. In 2007, the workshop was renamed to SemEval, and in the following five SemEval workshops (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) the nature of the tasks evolved to include semantic analysis tasks beyond word sense disambiguation. In 2012, SemEval turned into a yearly event. It currently runs every year, but on a two-year cycle, i.e., the tasks for SemEval-2015 were proposed in 2014. We are grateful to all task organisers (who organised 17 tasks!) and especially to the task participants whose massive participation (there were about 200 teams who submitted about 600 runs!) has made SemEval once again a successful event. We are thankful to those task organisers who also served as area chairs, and to those task organisers and task participants who helped with reviewing papers by their peers submitted to SemEval-2015: thanks for all the efforts, and for the high-quality, elaborate and thoughtful reviews! The papers in this proceedings have surely benefited from this feedback. We also thank the NAACL'2015 conference organizers for the local organization and the forum. Finally, we most gratefully acknowledge the support of our sponsor, the ACL Special Interest Group on the Lexicon (SIGLEX). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 ", "cite_spans": [ { "start": 156, "end": 178, "text": "SensEval-2 (2001), and", "ref_id": null }, { "start": 179, "end": 196, "text": "SensEval-3 (2004)", "ref_id": null }, { "start": 452, "end": 458, "text": "(2007)", "ref_id": null }, { "start": 459, "end": 465, "text": "(2008)", "ref_id": null }, { "start": 466, "end": 472, "text": "(2009)", "ref_id": null }, { "start": 473, "end": 479, "text": "(2010)", "ref_id": null }, { "start": 480, "end": 486, "text": "(2011)", "ref_id": null }, { "start": 487, "end": 493, "text": "(2012)", "ref_id": null }, { "start": 494, "end": 500, "text": "(2013)", "ref_id": null }, { "start": 501, "end": 507, "text": "(2014)", "ref_id": null }, { "start": 1582, "end": 1701, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Task 16 was cancelled after acceptance, but we kept the original numbering", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "Thursday, June 4, 2015 08:00-08:30 Registration 08:30-09:00 Opening remarks 09:00-10:00 Joint *SEM and SemEval keynote talk by Marco Baroni, \"Playing ficles and running with the corbons: What (multimodal) distributional semantic models learn during their childhood\" ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conference Program", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, 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Silva and Isabel Trancoso KLUEless: Polarity Classification and Association Nataliia Plotnikova, Micha Kohl, Kevin Volkert, Stefan Evert, Andreas Lerner, Na- talie Dykes and Heiko Ermer SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification Ruth Talbot, Chloe Acheampong and Richard Wicentowski SWATCS65: Sentiment Classification Using an Ensemble of Class Projects Richard Wicentowski SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression Yousef Alhessi and Richard Wicentowski TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis William Boag, Peter Potash and Anna Rumshisky SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning Prerna Chikersal, Soujanya Poria and Erik Cambria INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Re- sources using Embedding Subspaces Ram\u00f3n Astudillo, Silvio Amir, Wang Ling, Bruno Martins, Mario J. Silva and Isabel Trancoso WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition Richard Townsend, Adam Tsakalidis, Yiwei Zhou, Bo Wang, Maria Liakata, Arkaitz Zubiaga, Alexandra Cristea and Rob Procter UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015", "links": null }, "BIBREF134": { "ref_id": "b134", "title": "Identifying Sentiment Intensity in Ironic Tweets Hongzhi Xu, Enrico Santus, Anna Laszlo and Chu-Ren Huang KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter Hoang Long Nguyen, Trung Duc Nguyen, Dosam Hwang and Jason J. Jung LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally Cynthia Van Hee, Els Lefever and Veronique Hoste PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval", "authors": [ { "first": "Thursday ; Daniel", "middle": [], "last": "May", "suffix": "" }, { "first": "Noah", "middle": [], "last": "Weinthal", "suffix": "" }, { "first": "Richard", "middle": [], "last": "Wicentowski", "suffix": "" }, { "first": "Llt-Polyu", "middle": [], "last": "", "suffix": "" } ], "year": 2015, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Thursday, June 4, 2015 (continued) SWAT-CMW: Classification of Twitter Emotional Polarity using a Multiple- Classifier Decision Schema and Enhanced Emotion Tagging Riley Collins, Daniel May, Noah Weinthal and Richard Wicentowski LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets Hongzhi Xu, Enrico Santus, Anna Laszlo and Chu-Ren Huang KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter Hoang Long Nguyen, Trung Duc Nguyen, Dosam Hwang and Jason J. Jung LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally Cynthia Van Hee, Els Lefever and Veronique Hoste PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11", "links": null }, "BIBREF135": { "ref_id": "b135", "title": "Sentiment analysis of Figurative Language on Twitter #easypeasy #not Sarah McGillion, H\u00e9ctor Mart\u00ednez Alonso and Barbara Plank UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter Francesco Barbieri, Francesco Ronzano and Horacio Saggion DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter Maria Karanasou, Christos Doulkeridis and Maria Halkidi V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task", "authors": [ { "first": "Parth", "middle": [], "last": "Gupta", "suffix": "" }, { "first": "Jon Ander G\u00f3mez Valento ; Emilio", "middle": [], "last": "Sulis", "suffix": "" }, { "first": "Viviana", "middle": [], "last": "Patti", "suffix": "" }, { "first": "Giancarlo", "middle": [], "last": "Ruffo", "suffix": "" }, { "first": "Cristina", "middle": [], "last": "Bosco", "suffix": "" }, { "first": "Cph", "middle": [], "last": "", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Parth Gupta and Jon Ander G\u00f3mez ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm Delia Iraz\u00fa Hern\u00e1ndez Far\u00edas, Emilio Sulis, Viviana Patti, Giancarlo Ruffo and Cristina Bosco CPH: Sentiment analysis of Figurative Language on Twitter #easypeasy #not Sarah McGillion, H\u00e9ctor Mart\u00ednez Alonso and Barbara Plank UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figu- rative Language in Twitter Francesco Barbieri, Francesco Ronzano and Horacio Saggion DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter Maria Karanasou, Christos Doulkeridis and Maria Halkidi V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12", "links": null }, "BIBREF136": { "ref_id": "b136", "title": "Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis Orphee De Clercq, Marjan Van de Kauter, Els Lefever and Veronique Hoste UFRGS: Identifying Categories and Targets in Customer Reviews Anderson Kauer and Viviane Moreira", "authors": [ { "first": "Montse", "middle": [], "last": "Aitor Garc\u00eda Pablos", "suffix": "" }, { "first": "German", "middle": [], "last": "Cuadros", "suffix": "" }, { "first": "Lt3", "middle": [], "last": "Rigau", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Aitor Garc\u00eda Pablos, Montse Cuadros and German Rigau LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis Orphee De Clercq, Marjan Van de Kauter, Els Lefever and Veronique Hoste UFRGS: Identifying Categories and Targets in Customer Reviews Anderson Kauer and Viviane Moreira", "links": null }, "BIBREF138": { "ref_id": "b138", "title": "SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines Tommaso Caselli, Antske Fokkens, Roser Morante and Piek Vossen 09:25-09:40 SemEval-2015 Task 5: QA TempEval -Evaluating Temporal Information Understanding with Question Answering Hector Llorens, Nathanael Chambers", "authors": [ { "first": "Anne-Lyse", "middle": [], "last": "Minard", "suffix": "" }, { "first": "Manuela", "middle": [], "last": "Speranza", "suffix": "" }, { "first": "Eneko", "middle": [], "last": "Agirre", "suffix": "" }, { "first": "Itziar", "middle": [], "last": "Aldabe", "suffix": "" }, { "first": "Bernardo", "middle": [], "last": "Marieke Van Erp", "suffix": "" }, { "first": "German", "middle": [], "last": "Magnini", "suffix": "" }, { "first": "Ruben", "middle": [], "last": "Rigau", "suffix": "" }, { "first": "", "middle": [], "last": "Urizar", "suffix": "" } ], "year": null, "venue": "15 BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge", "volume": "6", "issue": "", "pages": "5--10", "other_ids": {}, "num": null, "urls": [], "raw_text": "Session SE5: Track II -Time and Space (Part 1) 09:00-09:15 SemEval-2015 Task 4: TimeLine: Cross-Document Event Ordering Anne-Lyse Minard, Manuela Speranza, Eneko Agirre, Itziar Aldabe, Marieke van Erp, Bernardo Magnini, German Rigau and Ruben Urizar 09:15-09:25 SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines Tommaso Caselli, Antske Fokkens, Roser Morante and Piek Vossen 09:25-09:40 SemEval-2015 Task 5: QA TempEval -Evaluating Temporal Information Under- standing with Question Answering Hector Llorens, Nathanael Chambers, Naushad UzZaman, Nasrin Mostafazadeh, James Allen and James Pustejovsky 09:40-09:50 HLT-FBK: a Complete Temporal Processing System for QA TempEval Paramita Mirza and Anne-Lyse Minard 09:50-10:05 SemEval-2015 Task 6: Clinical TempEval Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky and Marc Verhagen 10:05-10:15 BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Chal- lenge Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen and Wendy Chapman 10:15-11:00 Poster Session: Tasks 4, 5, 6, 7, and 8 (Part 1) GPLSIUA: Combining Temporal Information and Topic Modeling for Cross- Document Event Ordering Borja Navarro and Estela Saquete HeidelToul: A Baseline Approach for Cross-document Event Ordering Bilel Moulahi, Jannik Str\u00f6tgen, Michael Gertz and Lynda Tamine HITSZ-ICRC: An Integration Approach for QA TempEval Challenge Yongshuai Hou, Cong Tan, Qingcai Chen and Xiaolong Wang", "links": null }, "BIBREF139": { "ref_id": "b139", "title": "Olatz Arregi and Be\u00f1at Zapirain UTD: Ensemble-Based Spatial Relation Extraction Jennifer D'Souza and Vincent Ng SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines Tommaso Caselli, Antske Fokkens, Roser Morante and Piek Vossen HLT-FBK: a Complete Temporal Processing System for QA TempEval Paramita Mirza and Anne-Lyse Minard BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge Sumithra Velupillai", "authors": [ { "first": "Genevieve", "middle": [], "last": "Friday", "suffix": "" }, { "first": "Angus", "middle": [], "last": "Gorrell", "suffix": "" }, { "first": "Leon", "middle": [], "last": "Roberts", "suffix": "" }, { "first": "Marcos Didonet Del Fabro", "middle": [], "last": "Derczynski", "suffix": "" }, { "first": ";", "middle": [], "last": "Ixagroupehudiac", "suffix": "" }, { "first": "L", "middle": [], "last": "Danielle", "suffix": "" }, { "first": "Samir", "middle": [], "last": "Mowery", "suffix": "" }, { "first": "Lee", "middle": [], "last": "Abdelrahman", "suffix": "" }, { "first": "Wendy", "middle": [], "last": "Christensen", "suffix": "" }, { "first": "Ucd", "middle": [], "last": "Chapman", "suffix": "" } ], "year": 2015, "venue": "Multiple Approach System towards the Diachronic Evaluation of Texts Haritz Salaberri, Iker Salaberri, Olatz Arregi and Be\u00f1at Zapirain USAAR-CHRONOS: Crawling the Web for Temporal Annotations Liling Tan and Noam Ordan AMBRA: A Ranking Approach to Temporal Text Classification Marcos Zampieri, Alina Maria Ciobanu, Vlad Niculae and Liviu P. Dinu IXAGroupEHUSpaceEval: (X-Space) A WordNet-based approach towards the Automatic Recognition of Spatial Information following the ISO-Space Annotation Scheme Haritz Salaberri", "volume": "11", "issue": "", "pages": "40--54", "other_ids": {}, "num": null, "urls": [], "raw_text": "Friday, June 5, 2015 (continued) UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval Hegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski and Marcos Didonet Del Fabro IXAGroupEHUDiac: A Multiple Approach System towards the Diachronic Evalua- tion of Texts Haritz Salaberri, Iker Salaberri, Olatz Arregi and Be\u00f1at Zapirain USAAR-CHRONOS: Crawling the Web for Temporal Annotations Liling Tan and Noam Ordan AMBRA: A Ranking Approach to Temporal Text Classification Marcos Zampieri, Alina Maria Ciobanu, Vlad Niculae and Liviu P. Dinu IXAGroupEHUSpaceEval: (X-Space) A WordNet-based approach towards the Au- tomatic Recognition of Spatial Information following the ISO-Space Annotation Scheme Haritz Salaberri, Olatz Arregi and Be\u00f1at Zapirain UTD: Ensemble-Based Spatial Relation Extraction Jennifer D'Souza and Vincent Ng SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines Tommaso Caselli, Antske Fokkens, Roser Morante and Piek Vossen HLT-FBK: a Complete Temporal Processing System for QA TempEval Paramita Mirza and Anne-Lyse Minard BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Chal- lenge Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen and Wendy Chapman UCD : Diachronic Text Classification with Character, Word, and Syntactic N-grams Terrence Szymanski and Gerard Lynch SpRL-CWW: Spatial Relation Classification with Independent Multi-class Models Eric Nichols and Fadi Botros 10:30-11:00 Coffee Break and Poster Session Friday, June 5, 2015 (continued) Session SE6: Track II -Time and Space (Part 2) 11:00-11:15 SemEval 2015, Task 7: Diachronic Text Evaluation Octavian Popescu and Carlo Strapparava 11:15-11:25 UCD : Diachronic Text Classification with Character, Word, and Syntactic N-grams Terrence Szymanski and Gerard Lynch 11:25-11:40 SemEval-2015 Task 8: SpaceEval James Pustejovsky, Parisa Kordjamshidi, Marie-Francine Moens, Aaron Levine, Seth Dworman and Zachary Yocum 11:40-11:50 SpRL-CWW: Spatial Relation Classification with Independent Multi-class Models Eric Nichols and Fadi Botros 11:50-12:30 Poster Session: Tasks 4, 5, 6, 7, and 8 (Part 2) 12:30-14:00 Lunch Break Session SE6: Track V -Learning Semantic Relations 14:00-14:15 SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval) Georgeta Bordea, Paul Buitelaar, Stefano Faralli and Roberto Navigli 14:15-14:25 INRIASAC: Simple Hypernym Extraction Methods Gregory Grefenstette 14:25-14:40 SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinkova, Dan Flickinger, Jan Hajic and Zdenka Uresova 14:40-14:50 Peking: Building Semantic Dependency Graphs with a Hybrid Parser Yantao Du, Fan Zhang, Xun Zhang, Weiwei Sun and Xiaojun Wan 14:50-16:00 Poster Session: Tasks 17 and 18", "links": null } }, "ref_entries": { "FIGREF0": { "num": null, "text": "SemEval-2015 was co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT'2015) in Denver, Colorado. It included the following 17 shared tasks 1 organized in five tracks:\u2022 Text Similarity and Question Answering TRACK -Task 1: Paraphrase and Semantic Similarity in Twitter -Task 2: Semantic Textual Similarity -Task 3: Answer Selection in Community Question Answering \u2022 Time and Space TRACK -Task 4: TimeLine: Cross-Document Event Ordering -Task 5: QA TempEval -Task 6: Clinical TempEval -Task 7: Diachronic Text Evaluation -Task 8: SpaceEval \u2022 Sentiment TRACK -Task 9: CLIPEval Implicit Polarity of Events -Task 10: Sentiment Analysis in Twitter -Task 11: Sentiment Analysis of Figurative Language in Twitter -Task 12: Aspect Based Sentiment Analysis \u2022 Word Sense Disambiguation and Induction TRACK -Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking -Task 14: Analysis of Clinical Text -Task 15: A CPA Dictionary-Entry-Building Task \u2022 Learning Semantic Relations TRACK -Task 17: Taxonomy Extraction Evaluation -Task 18: Semantic Dependency ParsingThis volume contains both Task Description papers that describe each of the above tasks and System Description papers that describe the systems that participated in the above tasks. A total of 17 task description papers and 145 system description papers are included in this volume.", "uris": null, "type_str": "figure" }, "TABREF0": { "content": "
SemEval-2015 Chairs: Daniel Cer, Google David Jurgens, McGill University Preslav Nakov, Qatar Computing Research Institute Torsten Zesch, University of Duisburg-Essen CIS-positive: A DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity
SemEval-2015 Task 2
The SemEval-2015 organizers,
Daniel Cer, David Jurgens, Preslav Nakov and Torsten Zesch
", "text": "Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter Sebastian Ebert, Ngoc Thang Vu and Hinrich Sch\u00fctze .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 CLaC-SentiPipe: SemEval2015 Subtasks 10 B,E, and Task 11 Canberk \u00d6zdemir and Sabine Bergler .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing Chad Mills and Gina-Anne Levow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 CoMiC: Adapting a Short Answer Assessment System for Answer Selection Bj\u00f6rn Rudzewitz and Ramon Ziai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 CPH: Sentiment analysis of Figurative Language on Twitter #easypeasy #not Sarah McGillion, H\u00e9ctor Mart\u00ednez Alonso and Barbara Plank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 CUAB: Supervised Learning of Disorders and their Attributes using Relations James Gung, John Osborne and Steven Bethard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Piyush Arora, Chris Hokamp, Jennifer Foster and Gareth Jones . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation Dirk Weissenborn, Feiyu Xu and Hans Uszkoreit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter Abeed Sarker, Azadeh Nikfarjam, Davy Weissenbacher and Graciela Gonzalez . . . . . . . . . . . . . 510 DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition Md Arafat Sultan, Steven Bethard and Tamara Sumner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter Maria Karanasou, Christos Doulkeridis and Maria Halkidi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709 Duluth: Word Sense Discrimination in the Service of Lexicography Ted Pedersen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams Taneeya Satyapanich, Hang Gao and Tim Finin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 EBL-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based Technique Eniafe Festus Ayetiran and Guido Boella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews Zhihua Zhang and Man Lan .", "type_str": "table", "num": null, "html": null } } } }