Table of Contents

• Front Matter [doi]
• SemEval-2016 Task 12: Clinical TempEvalSteven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky, Marc Verhagen. [doi]
• RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related StatisticsErgun Biçici. [doi]
• SemEval-2016 Task 4: Sentiment Analysis in TwitterPreslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov. 1-18 [doi]
• SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused EvaluationDaniel M. Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, Lucia Specia. 1-14 [doi]
• SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word SimilarityJosé Camacho-Collados, Mohammad Taher Pilehvar, Nigel Collier, Roberto Navigli. 15-26 [doi]
• SemEval-2016 Task 5: Aspect Based Sentiment AnalysisMaria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia V. Loukachevitch, Evgeniy Kotelnikov, Núria Bel, Salud María Jiménez Zafra, Gülsen Eryigit. 19-30 [doi]
• SemEval-2017 Task 3: Community Question AnsweringPreslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin, Karin Verspoor. 27-48 [doi]
• SemEval-2016 Task 6: Detecting Stance in TweetsSaif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiao-Dan Zhu, Colin Cherry. 31-41 [doi]
• SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic PhrasesSvetlana Kiritchenko, Saif Mohammad, Mohammad Salameh. 42-51 [doi]
• SemEval-2017 Task 6: #HashtagWars: Learning a Sense of HumorPeter Potash, Alexey Romanov, Anna Rumshisky. 49-57 [doi]
• CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment ClassificationMahmoud Nabil, Amir Atyia, Mohamed A. Aly. 52-57 [doi]
• QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal QuantificationGiovanni Da San Martino, Wei Gao, Fabrizio Sebastiani 0001. 58-63 [doi]
• SemEval-2017 Task 7: Detection and Interpretation of English PunsTristan Miller, Christian Hempelmann, Iryna Gurevych. 58-68 [doi]
• SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment ClassificationStefan Räbiger, Mishal Kazmi, Yücel Saygin, Peter Schüller, Myra Spiliopoulou. 64-70 [doi]
• SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumoursLeon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, Arkaitz Zubiaga. 69-76 [doi]
• I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in TwitterZhengchen Zhang, Chen Zhang, Fuxiang Wu, Dong-Yan Huang, Weisi Lin, Minghui Dong. 71-78 [doi]
• BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual SimilarityHao Wu, Heyan Huang, Ping Jian, Yuhang Guo, Chao Su. 77-84 [doi]
• LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and QuantificationDavid Vilares, Yerai Doval, Miguel A. Alonso, Carlos Gómez-Rodríguez. 79-84 [doi]
• ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational KnowledgeRobert Speer, Joanna Lowry-Duda. 85-89 [doi]
• TwiSE at SemEval-2016 Task 4: Twitter Sentiment ClassificationGeorgios Balikas, Massih-Reza Amini. 85-91 [doi]
• IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue IdentificationTitas Nandi, Chris Biemann, Seid Muhie Yimam, Deepak Gupta, Sarah Kohail, Asif Ekbal, Pushpak Bhattacharyya. 90-97 [doi]
• ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal ScaleAndrea Esuli. 92-95 [doi]
• aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment AnalysisStavros Giorgis, Apostolos Rousas, John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos. 96-99 [doi]
• HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor RecognitionDavid Donahue, Alexey Romanov, Anna Rumshisky. 98-102 [doi]
• thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point ScaleVikrant Yadav. 100-102 [doi]
• Idiom Savant at Semeval-2017 Task 7: Detection and Interpretation of English PunsSamuel Doogan, Aniruddha Ghosh, Hanyang Chen, Tony Veale. 103-108 [doi]
• NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment AnalysisBrage Ekroll Jahren, Valerij Fredriksen, Björn Gambäck, Lars Bungum. 103-108 [doi]
• UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based RepresentationEsteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez. 109-114 [doi]
• CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual SimilarityJérémy Ferrero, Laurent Besacier, Didier Schwab, Frédéric Agnès. 109-114 [doi]
• GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised SystemJonathan Juncal-Martínez, Tamara Álvarez-López, Milagros Fernández Gavilanes, Enrique Costa-Montenegro, Francisco Javier González Castaño. 115-119 [doi]
• UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesHussein T. Al-Natsheh, Lucie Martinet, Fabrice Muhlenbach, Djamel Abdelkader Zighed. 115-119 [doi]
• Aicyber at SemEval-2016 Task 4: i-vector based sentence representationSteven Du, Xi Zhang. 120-125 [doi]
• DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model OutputNabin Maharjan, Rajendra Banjade, Dipesh Gautam, Lasang Jimba Tamang, Vasile Rus. 120-124 [doi]
• FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity ApproachBasma Hassan, Samir E. AbdelRahman, Reem Bahgat, Ibrahim Farag. 125-129 [doi]
• PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment AnalysisMateusz Lango, Dariusz Brzezinski, Jerzy Stefanowski. 126-132 [doi]
• HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual SimilarityYang Shao. 130-133 [doi]
• mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in TwitterVittoria Cozza, Marinella Petrocchi. 133-138 [doi]
• LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors WeightingEl Moatez Billah Nagoudi, Jérémy Ferrero, Didier Schwab. 134-138 [doi]
• OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarityMartyna Rpiewak, Piotr Sobecki, Daniel Karas. 139-143 [doi]
• MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity ClassificationHang Gao, Tim Oates. 139-144 [doi]
• Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic SimilarityCristina España-Bonet, Alberto Barrón-Cedeño. 144-149 [doi]
• CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced EmbeddingsHelena Gómez-Adorno, Darnes Vilariño, Grigori Sidorov, David Pinto Avendaño. 145-148 [doi]
• Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment AnalysisDario Stojanovski, Gjorgji Strezoski, Gjorgji Madjarov, Ivica Dimitrovski. 149-154 [doi]
• QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word EmbeddingsFanqing Meng, Wenpeng Lu, Yuteng Zhang, Jinyong Cheng, Yuehan Du, Shuwang Han. 150-153 [doi]
• ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual SimilarityJohannes Bjerva, Robert Östling. 154-158 [doi]
• Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model AdaptationElisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Nikolaos Malandrakis, Haris Papageorgiou, Shrikanth Narayanan, Alexandros Potamianos. 155-163 [doi]
• ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity ComputingWenjie Liu, Chengjie Sun, Lei Lin, Bingquan Liu. 159-163 [doi]
• Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity ModelWenli Zhuang, Ernie Chang. 164-169 [doi]
• UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment ClassificationOmar Abdelwahab, Adel Elmaghraby. 164-170 [doi]
• SEF$@$UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph VectorMirela-Stefania Duma, Wolfgang Menzel. 170-174 [doi]
• NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification LibraryNikolay Karpov, Alexander Porshnev, Kirill Rudakov. 171-177 [doi]
• STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised EnsembleSarah Kohail, Amr Rekaby Salama, Chris Biemann. 175-179 [doi]
• INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and QuantificationSebastian Ruder, Parsa Ghaffari, John G. Breslin. 178-182 [doi]
• UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual SimilarityJoe Barrow, Denis Peskov. 180-184 [doi]
• UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment ClassificationSteven Xu, Huizhi Liang, Timothy Baldwin. 183-189 [doi]
• MITRE at SemEval-2017 Task 1: Simple Semantic SimilarityJohn C. Henderson, Elizabeth M. Merkhofer, Laura Strickhart, Guido Zarrella. 185-190 [doi]
• SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in TwitterHussam Hamdan. 190-197 [doi]
• ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual SimilarityJunfeng Tian, Zhiheng Zhou, Man Lan, Yuanbin Wu. 191-197 [doi]
• PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event EmbeddingsI-Ta Lee, Mahak Goindani, Chang Li, Di Jin, Kristen Johnson, Xiao Zhang, Maria Leonor Pacheco, Dan Goldwasser. 198-202 [doi]
• DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine ApproachVictor Martinez Morant, Lluís F. Hurtado, Ferran Pla. 198-201 [doi]
• SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysisMickael Rouvier, Benoît Favre. 202-208 [doi]
• RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic SimilarityErgun Biçici. 203-207 [doi]
• LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual SimilarityIgnacio Arroyo-Fernández, Ivan Vladimir Meza Ruiz. 208-212 [doi]
• DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment LexiconsAbeed Sarker, Graciela Gonzalez. 209-214 [doi]
• L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarityPedro Fialho, Hugo Rodrigues 0001, Luísa Coheur, Paulo Quaresma. 213-219 [doi]
• VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in TwitterGerard Briones, Kasun Amarasinghe, Bridget T. McInnes. 215-219 [doi]
• UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment ClassificationGiuseppe Attardi, Daniele Sartiano. 220-224 [doi]
• HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic SimilarityJunqing He, Long Wu, Xuemin Zhao, YongHong Yan. 220-225 [doi]
• IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of TweetsJasper Friedrichs. 225-229 [doi]
• Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based ContextsPablo Gamallo 0001. 226-229 [doi]
• Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarityJosué Melka, Gilles Bernard. 230-234 [doi]
• PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural NetworksUladzimir Sidarenka. 230-237 [doi]
• QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge BaseFanqing Meng, Wenpeng Lu, Yuteng Zhang, Ping Jian, ShuMin Shi, Heyan Huang. 235-238 [doi]
• INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding WordsSilvio Amir, Ramón Fernández Astudillo, Wang Ling, Mário J. Silva, Isabel Trancoso. 238-242 [doi]
• RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like listSergio Jiménez 0001, George Dueñas, Lorena Gaitan, Jorge Segura. 239-244 [doi]
• SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyardCosmin Florean, Oana Bejenaru, Eduard Apostol, Octavian Ciobanu, Adrian Iftene, Diana Trandabat. 243-246 [doi]
• MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approachEnrico Mensa, Daniele P. Radicioni, Antonio Lieto. 245-249 [doi]
• Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?Calin-Cristian Ciubotariu, Marius-Valentin Hrisca, Mihail Gliga, Diana Darabana, Diana Trandabat, Adrian Iftene. 247-250 [doi]
• HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity AssessmentBehrang QasemiZadeh, Laura Kallmeyer. 250-255 [doi]
• YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional NetworkYunchao He, Liang-Chih Yu, Chin-Sheng Yang, K. Robert Lai, Weiyi Liu. 251-255 [doi]
• ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in TwitterYunxiao Zhou, Zhihua Zhang, Man Lan. 256-261 [doi]
• Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word SimilarityNiloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hosseini pour, Aryan Vahid pour. 256-260 [doi]
• Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched WikipediaClaudio Delli Bovi, Alessandro Raganato. 261-266 [doi]
• OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" WorldAlexandra Balahur. 262-265 [doi]
• Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression ExtractionStefan Falk, Andi Rexha, Roman Kern. 266-270 [doi]
• Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word SimilarityRazvan-Gabriel Rotari, Ionut Hulub, Stefan Oprea, Mihaela Plamada-Onofrei, Alina Beatrice Lorent, Raluca Preisler, Adrian Iftene, Diana Trandabat. 267-270 [doi]
• NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment ExtractionTalaat Khalil, Samhaa R. El-Beltagy. 271-276 [doi]
• TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question AnswersMohammed R. H. Qwaider, Abed Alhakim Freihat, Fausto Giunchiglia. 271-274 [doi]
• UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for ArabicYassine El Adlouni, Imane Lahbari, Horacio Rodríguez, Mohammed Meknassi, Said Ouatik El Alaoui, Noureddine Ennahnahi. 275-279 [doi]
• XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment AnalysisCaroline Brun, Julien Perez, Claude Roux. 277-281 [doi]
• Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question AnsweringWenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou. 280-286 [doi]
• NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network FeaturesZhiqiang Toh, Jian Su. 282-288 [doi]
• MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking TasksMiguel J. Rodrigues, Francisco M. Couto. 287-291 [doi]
• bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment AnalysisToshihiko Yanase, Kohsuke Yanai, Misa Sato, Toshinori Miyoshi, Yoshiki Niwa. 289-295 [doi]
• EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question AnsweringYufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, Zhao Lu. 292-298 [doi]
• IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of SentenceMaryna Chernyshevich. 296-300 [doi]
• FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question AnsweringGiuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto. 299-304 [doi]
• BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category DetectionJakub Machacek. 301-305 [doi]
• SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question SimilarityLe Qi, Yu Zhang, Ting Liu. 305-309 [doi]
• GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment AnalysisTamara Álvarez-López, Jonathan Juncal-Martínez, Milagros Fernández Gavilanes, Enrique Costa-Montenegro, Francisco Javier González Castaño. 306-311 [doi]
• LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich FeaturesNaman Goyal. 310-314 [doi]
• AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment AnalysisDionysios Xenos, Panagiotis Theodorakakos, John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos. 312-317 [doi]
• SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question AnsweringDelphine Charlet, Géraldine Damnati. 315-319 [doi]
• AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer ReviewsShubham Pateria, Prafulla Choubey. 318-324 [doi]
• FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question AnsweringSheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, Zhaoyun Ding. 320-325 [doi]
• MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in RussianVladimir Mayorov, Ivan Andrianov. 325-329 [doi]
• KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question AnsweringSimone Filice, Giovanni Da San Martino, Alessandro Moschitti. 326-333 [doi]
• INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment AnalysisSebastian Ruder, Parsa Ghaffari, John G. Breslin. 330-336 [doi]
• SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question AnsweringJan Deriu, Mark Cieliebak. 334-338 [doi]
• TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment AnalysisFatih Samet Çetin, Ezgi Yildirim, Can Özbey, Gülsen Eryigit. 337-341 [doi]
• TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QAFilip Saina, Toni Kukurin, Lukrecija Puljic, Mladen Karan, Jan Snajder. 339-343 [doi]
• UWB at SemEval-2016 Task 5: Aspect Based Sentiment AnalysisTomás Hercig, Tomás Brychcín, Lukás Svoboda, Michal Konkol. 342-349 [doi]
• GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic ForaNada AlMarwani, Mona T. Diab. 344-348 [doi]
• NLM_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question AnsweringAsma Ben Abacha, Dina Demner-Fushman. 349-352 [doi]
• SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity DetectionHussam Hamdan. 350-355 [doi]
• bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility FeaturesYuta Koreeda, Takuya Hashito, Yoshiki Niwa, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi, Kohsuke Yanai. 353-359 [doi]
• COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure TheoryKim Schouten, Flavius Frasincar. 356-360 [doi]
• QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering ForumsMarwan Torki, Maram Hasanain, Tamer Elsayed. 360-364 [doi]
• ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in ReviewsMengxiao Jiang, Zhihua Zhang, Man Lan. 361-366 [doi]
• ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering TaskGuoShun Wu, Yixuan Sheng, Man Lan, Yuanbin Wu. 365-369 [doi]
• UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence ClassificationAles Tamchyna, Katerina Veselovská. 367-371 [doi]
• UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQASurya Agustian, Hiroya Takamura. 370-374 [doi]
• UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment AnalysisOlga Vechtomova, Anni He. 372-377 [doi]
• Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase DetectionByron Galbraith, Bhanu Pratap, Daniel Shank. 375-379 [doi]
• INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in TweetsMarcelo Dias, Karin Becker. 378-383 [doi]
• QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in TwitterXiwu Han, Gregory Toner. 380-384 [doi]
• pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance DetectionWan Wei, Xiao Zhang, Xuqin Liu, Wei Chen, Tengjiao Wang. 384-388 [doi]
• Duluth at SemEval-2017 Task 6: Language Models in Humor DetectionXinru Yan, Ted Pedersen. 385-389 [doi]
• USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with AutoencodersIsabelle Augenstein, Andreas Vlachos, Kalina Bontcheva. 389-393 [doi]
• DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text ComparisonChristos Baziotis, Nikos Pelekis, Christos Doulkeridis. 390-395 [doi]
• IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in TwitterCan Liu, Wen Li, Bradford Demarest, Yue Chen, Sara Couture, Daniel Dakota, Nikita Haduong, Noah Kaufman, Andrew Lamont, Manan Pancholi, Kenneth Steimel, Sandra Kübler. 394-400 [doi]
• TakeLab at SemEval-2017 Task 6: #RankingHumorIn4PagesMarin Kukovacec, Juraj Malenica, Ivan Mrsic, Antonio Sajatovic, Domagoj Alagic, Jan Snajder. 396-400 [doi]
• SRHR at SemEval-2017 Task 6: Word Associations for Humour RecognitionAndrew Cattle, Xiaojuan Ma. 401-406 [doi]
• Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance DetectionYuki Igarashi, Hiroya Komatsu, Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui. 401-407 [doi]
• #WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous TweetsIuliana Alexandra Flescan-Lovin-Arseni, Ramona Andreea Turcu, Cristina Sirbu, Larisa Alexa, Sandra Maria Amarandei, Nichita Herciu, Constantin Scutaru, Diana Trandabat, Adrian Iftene. 407-410 [doi]
• UWB at SemEval-2016 Task 6: Stance DetectionPeter Krejzl, Josef Steinberger. 408-412 [doi]
• SVNIT $@$ SemEval 2017 Task-6: Learning a Sense of Humor Using Supervised ApproachRutal Mahajan, Mukesh Zaveri. 411-415 [doi]
• DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNsPrashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Roy. 413-419 [doi]
• Duluth at SemEval-2017 Task 7 : Puns Upon a Midnight Dreary, Lexical Semantics for the Weak and WearyTed Pedersen. 416-420 [doi]
• NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in TweetsAmita Misra, Brian Ecker, Theodore Handleman, Nicolas Hahn, Marilyn A. Walker. 420-427 [doi]
• UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based MetricsOlga Vechtomova. 421-425 [doi]
• PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in Automatic Pun Recognition and InterpretationElena Mikhalkova, Yuri Karyakin. 426-431 [doi]
• ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked ClassifiersMichael Wojatzki, Torsten Zesch. 428-433 [doi]
• JU CSE NLP $@$ SemEval 2017 Task 7: Employing Rules to Detect and Interpret English PunsAniket Pramanick, Dipankar Das. 432-435 [doi]
• CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal TextHeba Elfardy, Mona T. Diab. 434-439 [doi]
• N-Hance at SemEval-2017 Task 7: A Computational Approach using Word Association for Puns436-439 [doi]
• JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector MachinesBraja Gopal Patra, Dipankar Das, Sivaji Bandyopadhyay. 440-444 [doi]
• ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and InterpretationLluís F. Hurtado, Encarna Segarra, Ferran Pla, Pascual Carrasco, José-Àngel González. 440-443 [doi]
• BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense EmbeddingsDieke Oele, Kilian Evang. 444-448 [doi]
• IDI$@$NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word RepresentationHenrik Bøhler, Petter Asla, Erwin Marsi, Rune Sætre. 445-450 [doi]
• UWAV at SemEval-2017 Task 7: Automated feature-based system for locating punsAnkit Vadehra. 449-452 [doi]
• ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in TweetsZhihua Zhang, Man Lan. 451-457 [doi]
• ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English PunsYuhuan Xiu, Man Lan, Yuanbin Wu. 453-456 [doi]
• Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English LanguageVijayasaradhi Indurthi, Subba Reddy Oota. 457-460 [doi]
• MITRE at SemEval-2016 Task 6: Transfer Learning for Stance DetectionGuido Zarrella, Amy Marsh. 458-463 [doi]
• UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent FeaturesHareesh Bahuleyan, Olga Vechtomova. 461-464 [doi]
• TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based EnsembleMartin Tutek, Ivan Sekulic, Paula Gombar, Ivan Paljak, Filip Culinovic, Filip Boltuzic, Mladen Karan, Domagoj Alagic, Jan Snajder. 464-468 [doi]
• IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verificationYi-Chin Chen, Zhao-yang Liu, Hung-Yu Kao. 465-469 [doi]
• LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity PredictionAmal Htait, Sébastien Fournier, Patrice Bellot. 469-473 [doi]
• NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on TwitterOmar Enayet, Samhaa R. El-Beltagy. 470-474 [doi]
• iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter PhrasesEshrag Refaee, Verena Rieser. 474-480 [doi]
• Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM475-480 [doi]
• UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity DeterminationLadislav Lenc, Pavel Král, Václav Rajtmajer. 481-485 [doi]
• Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and RulesMarianela Garcia Lozano, Hanna Lilja, Edward Tjörnhammar, Maja Karasalo. 481-485 [doi]
• DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading HeuristicsAnkit Srivastava, Georg Rehm, Julián Moreno Schneider. 486-490 [doi]
• NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment TermsSamhaa R. El-Beltagy. 486-490 [doi]
• ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity RankingFeixiang Wang, Zhihua Zhang, Man Lan. 491-496 [doi]
• ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble ModelsFeixiang Wang, Man Lan, Yuanbin Wu. 491-496 [doi]
• SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual EvaluationEneko Agirre, Carmen Banea, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, Janyce Wiebe. 497-511 [doi]
• IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour EvaluationVikram Singh, Sunny Narayan, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya. 497-501 [doi]
• SemEval-2017 Task 4: Sentiment Analysis in TwitterSara Rosenthal, Noura Farra, Preslav Nakov. 502-518 [doi]
• SemEval-2016 Task 2: Interpretable Semantic Textual SimilarityEneko Agirre, Aitor Gonzalez-Agirre, Iñigo Lopez-Gazpio, Montse Maritxalar, German Rigau, Larraitz Uria. 512-524 [doi]
• SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and NewsKeith Cortis, André Freitas, Tobias Daudert, Manuela Hürlimann, Manel Zarrouk, Siegfried Handschuh, Brian Davis. 519-535 [doi]
• SemEval-2016 Task 3: Community Question AnsweringPreslav Nakov, Lluís Màrquez, Alessandro Moschitti, Walid Magdy, Hamdy Mubarak, Abed Alhakim Freihat, Jim Glass, Bilal Randeree. 525-545 [doi]
• SemEval-2017 Task 9: Abstract Meaning Representation Parsing and GenerationJonathan May, Jay Priyadarshi. 536-545 [doi]
• SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific PublicationsIsabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum. 546-555 [doi]
• SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)Nathan Schneider, Dirk Hovy, Anders Johannsen, Marine Carpuat. 546-559 [doi]
• SemEval-2017 Task 11: End-User Development using Natural LanguageJuliano Efson Sales, Siegfried Handschuh, André Freitas. 556-564 [doi]
• SemEval 2016 Task 11: Complex Word IdentificationGustavo Paetzold, Lucia Specia. 560-569 [doi]
• SemEval-2017 Task 12: Clinical TempEvalSteven Bethard, Guergana Savova, Martha Palmer, James Pustejovsky. 565-572 [doi]
• FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word EmbeddingsDuygu Ataman, José Guilherme Camargo de Souza, Marco Turchi, Matteo Negri. 570-576 [doi]
• BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMsMathieu Cliche. 573-580 [doi]
• VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic SimilaritySam Henry, Allison Sands. 577-583 [doi]
• Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlinesAndrew Moore, Paul Rayson. 581-585 [doi]
• UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and EvaluationPeng Li, Heng Huang. 584-587 [doi]
• Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMRGerasimos Lampouras, Andreas Vlachos. 586-591 [doi]
• UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic InformationTomás Brychcín, Lukás Svoboda. 588-594 [doi]
• The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extractionWaleed Ammar, Matthew E. Peters, Chandra Bhagavatula, Russell Power. 592-596 [doi]
• HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual SimilarityMatthias Liebeck, Philipp Pollack, Pashutan Modaresi, Stefan Conrad 0001. 595-601 [doi]
• LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical NarrativesJulien Tourille, Olivier Ferret, Xavier Tannier, Aurélie Névéol. 597-602 [doi]
• Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarityBarbara Rychalska, Katarzyna Pakulska, Krystyna Chodorowska, Wojciech Walczak, Piotr Andruszkiewicz. 602-608 [doi]
• OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based ModelRamy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El Khoury, Ahmad Al Sallab, Hazem Hajj, Nizar Habash, Khaled Shaban, Wassim El-Hajj. 603-610 [doi]
• USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a BoxAhmet Aker, Frédéric Blain, Andrés Duque, Marina Fomicheva, Jurica Seva, Kashif Shah, Daniel Beck. 609-613 [doi]
• NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment AnalysisEdilson Anselmo Corrêa Júnior, Vanessa Queiroz Marinho, Leandro Borges dos Santos. 611-615 [doi]
• NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level featuresPiotr Przybyla, Nhung T. H. Nguyen, Matthew Shardlow, Georgios Kontonatsios, Sophia Ananiadou. 614-620 [doi]
• deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in TwitterTzu-Hsuan Yang, Tzu-Hsuan Tseng, Chia-Ping Chen. 616-620 [doi]
• NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different EmbeddingsYichun Yin, Yangqiu Song, Ming Zhang. 621-625 [doi]
• ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual SimilarityJunfeng Tian, Man Lan. 621-627 [doi]
• CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and QuantificationRaj Kumar Gupta, Yinping Yang. 626-633 [doi]
• SAARSHEFF at SemEval-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (eXtreme) Boosted Tree EnsemblesLiling Tan, Carolina Scarton, Lucia Specia, Josef van Genabith. 628-633 [doi]
• WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual SimilarityHanna Béchara, Rohit Gupta, Liling Tan, Constantin Orasan, Ruslan Mitkov, Josef van Genabith. 634-639 [doi]
• SINAI at SemEval-2017 Task 4: User based classificationSalud María Jiménez Zafra, Arturo Montejo Ráez, Maria Teresa Martín-Valdivia, Luis Alfonso Ureña López. 634-639 [doi]
• HLP$@$UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectorsAbeed Sarker, Graciela Gonzalez. 640-643 [doi]
• DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional SemanticsRajendra Banjade, Nabin Maharjan, Dipesh Gautam, Vasile Rus. 640-644 [doi]
• ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in TwitterEnkhzol Dovdon, José Saias. 644-647 [doi]
• ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple FeaturesCheng Fu, Bo An, Xianpei Han, Le Sun. 645-649 [doi]
• SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment ClassificationRaphaël Troncy, Enrico Palumbo, Efstratios Sygkounas, Giuseppe Rizzo 0002. 648-652 [doi]
• DLS$@$CU at SemEval-2016 Task 1: Supervised Models of Sentence SimilarityMd. Arafat Sultan, Steven Bethard, Tamara Sumner. 650-655 [doi]
• Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on TwitterAlon Rozental, Daniel Fleischer. 653-658 [doi]
• DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual SimilarityChris Hokamp, Piyush Arora. 656-662 [doi]
• TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree ClassifierNaveen Kumar Laskari, Suresh Kumar Sanampudi. 659-663 [doi]
• GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STSHanan Aldarmaki, Mona T. Diab. 663-667 [doi]
• Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic TweetsHala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu. 664-669 [doi]
• CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual SimilarityChi-kiu Lo, Cyril Goutte, Michel Simard. 668-673 [doi]
• OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random FieldsChukwuyem Onyibe, Nizar Habash. 670-674 [doi]
• MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic ModelNaveed Afzal, Yanshan Wang, Hongfang Liu. 674-679 [doi]
• Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in TwitterAthanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Nikolaos Malandrakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan, Alexandros Potamianos. 675-682 [doi]
• UoB-UK at SemEval-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase LinkingHarish Tayyar Madabushi, Mark Buhagiar, Mark Lee. 680-685 [doi]
• NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning ArchitectureNikolay Karpov. 683-688 [doi]
• BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information ContentHao Wu, Heyan Huang, Wenpeng Lu. 686-690 [doi]
• MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment ClassificationJingjing Zhao, Yan Yang, Bing Xu. 689-693 [doi]
• RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity EstimationHideo Itoh. 691-695 [doi]
• SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of FeaturesMohammed Jabreel, Antonio Moreno. 694-699 [doi]
• IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic SimilarityMaryna Beliuha, Maryna Chernyshevich. 696-701 [doi]
• Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity ClassificationHussam Hamdan. 700-703 [doi]
• JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein RatioSandip Sarkar, Dipankar Das, Partha Pakray, Alexander F. Gelbukh. 702-705 [doi]
• DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment AnalysisSymeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis. 704-708 [doi]
• Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher DimensionBarathi Ganesh H. B., M. Anand Kumar, K. P. Soman. 706-711 [doi]
• SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process ClassifierAngel Deborah S, S. Milton Rajendram, T. T. Mirnalinee. 709-712 [doi]
• NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual SimilarityJohn Philip McCrae, Kartik Asooja, Nitish Aggarwal, Paul Buitelaar. 712-717 [doi]
• YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in TwitterMing Wang, Biao Chu, Qingxun Liu, Xiaobing Zhou. 713-717 [doi]
• NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text SimilarityKolawole Adebayo, Luigi Di Caro, Guido Boella. 718-725 [doi]
• LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity ClassificationAmal Htait, Sébastien Fournier, Patrice Bellot. 718-722 [doi]
• ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep LearningJosé-Àngel González, Ferran Pla, Lluís F. Hurtado. 723-727 [doi]
• LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategiesOscar William Lightgow Serrano, Iván Vladimir Meza Ruíz, Albert Manuel Orozco Camacho, Jorge García Flores, Davide Buscaldi. 726-731 [doi]
• XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in TwitterYazhou Hao, YangYang Lan, Yufei Li, Chen Li. 728-731 [doi]
• Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with AttentionJoosung Yoon, Kigon Lyu, Hyeoncheol Kim. 732-736 [doi]
• UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and EvaluationMilton King, Waseem Gharbieh, Sohyun Park, Paul Cook. 732-735 [doi]
• ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual SimilarityAsli Eyecioglu, Bill Keller. 736-740 [doi]
• EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment ClassificationMaoquan Wang, Chen Shiyun, Xie Yufei, Zhao Lu. 737-740 [doi]
• SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual SimilarityPeter Potash, William Boag, Alexey Romanov, Vasili Ramanishka, Anna Rumshisky. 741-748 [doi]
• funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target ContextsQuanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang, Sameena Shah. 741-746 [doi]
• DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisChristos Baziotis, Nikos Pelekis, Christos Doulkeridis. 747-754 [doi]
• SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual SimilaritySergio Jimenez. 749-757 [doi]
• TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and QuantificationGeorgios Balikas. 755-759 [doi]
• LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment ClassificationMickael Rouvier. 760-765 [doi]
• DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text SimilarityJie Mei, Aminul Islam, Evangelos E. Milios. 765-770 [doi]
• TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant SupervisionSimon Müller, Tobias Huonder, Jan Deriu, Mark Cieliebak. 766-770 [doi]
• iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STSIñigo Lopez-Gazpio, Eneko Agirre, Montse Maritxalar. 771-776 [doi]
• INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment AnalysisSabino Miranda-Jiménez, Mario Graff, Eric Sadit Tellez, Daniela Moctezuma. 771-776 [doi]
• BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN ApproachesDeger Ayata, Murat Saraclar, Arzucan Ozgur. 777-783 [doi]
• Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic EquivalencePing Tan, Karin Verspoor, Timothy Miller. 777-782 [doi]
• FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual SimilaritySimone Magnolini, Anna Feltracco, Bernardo Magnini. 783-789 [doi]
• TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in TwitterDavid Lozic, Doria Saric, Ivan Tokic, Zoran Medic, Jan Snajder. 784-789 [doi]
• IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk AlignerLavanya Tekumalla, Sharmistha Jat. 790-795 [doi]
• NileTMRG at SemEval-2017 Task 4: Arabic Sentiment AnalysisSamhaa R. El-Beltagy, Mona El kalamawy, Abu Bakr Soliman. 790-795 [doi]
• VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approachRodolfo Delmonte. 796-802 [doi]
• YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment ClassificationHaowei Zhang, Jin Wang, Jixian Zhang, Xuejie Zhang. 796-801 [doi]
• TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment AnalysisAmit Ajit Deshmane, Jasper Friedrichs. 802-806 [doi]
• UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for ChunksMiloslav Konopík, Ondrej Prazák, David Steinberger, Tomás Brychcín. 803-808 [doi]
• UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in TwitterJosé Abreu, Iván Castro, Claudia Martínez, Sebastián Oliva, Yoan Gutiérrez. 807-811 [doi]
• DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation PredictionRajendra Banjade, Nabin Maharjan, Nobal Bikram Niraula, Vasile Rus. 809-813 [doi]
• ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity ClassificationYunxiao Zhou, Man Lan, Yuanbin Wu. 812-816 [doi]
• UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question AnsweringMarc Franco-Salvador, Sudipta Kar, Thamar Solorio, Paolo Rosso. 814-821 [doi]
• Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News HeadlinesYouness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini, Jacopo Staiano. 817-822 [doi]
• RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text RankingAhmed Magooda, Amr Gomaa, Ashraf Y. Mahgoub, Hany Ahmed, Mohsen Rashwan, Hazem M. Raafat, Eslam Kamal, Ahmad A. Al Sallab. 822-827 [doi]
• SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression ModelAngel Deborah S, S. Milton Rajendram, T. T. Mirnalinee. 823-826 [doi]
• IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and NewsZarmeen Nasim. 827-831 [doi]
• SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question AnsweringMitra Mohtarami, Yonatan Belinkov, Wei-Ning Hsu, Yu Zhang, Tao Lei, Kfir Bar, Scott Cyphers, Jim Glass. 828-835 [doi]
• HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning MethodsTobias Cabanski, Julia Romberg, Stefan Conrad 0001. 832-836 [doi]
• SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question AnsweringTsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva, Todor Mihaylov, Momchil Hardalov, Yasen Kiprov, Daniel Balchev, Ivan Koychev, Preslav Nakov, Ivelina Nikolova, Galia Angelova. 836-843 [doi]
• INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and HeadlinesTiago Zini, Karin Becker, Marcelo Dias. 837-841 [doi]
• HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networksLidia Pivovarova, Llorenç Escoter, Arto Klami, Roman Yangarber. 842-846 [doi]
• PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question AnsweringDaniel Balchev, Yasen Kiprov, Ivan Koychev, Preslav Nakov. 844-850 [doi]
• NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and NewsChung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen. 847-851 [doi]
• UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity MeasuresTimothy Baldwin, Huizhi Liang, Bahar Salehi, Doris Hoogeveen, Yitong Li, Long Duong. 851-856 [doi]
• funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and TwitterQuanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang, Xiaomo Liu. 852-856 [doi]
• ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA SystemYunfang Wu, Minghua Zhang. 857-860 [doi]
• SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial TweetsNarges Tabari, Armin Seyeditabari, Wlodek Zadrozny. 857-860 [doi]
• DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News ArticlesSymeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, Avi Arampatzis. 861-865 [doi]
• Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word EmbeddingsHujie Wang, Pascal Poupart. 861-865 [doi]
• TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial newsLeon Rotim, Martin Tutek, Jan Snajder. 866-871 [doi]
• QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word EmbeddingRana Malhas, Marwan Torki, Tamer Elsayed. 866-871 [doi]
• UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset AugmentationVineet John, Olga Vechtomova. 872-876 [doi]
• ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question AnsweringGuoShun Wu, Man Lan. 872-878 [doi]
• RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural NetworksSudipta Kar, Suraj Maharjan, Thamar Solorio. 877-882 [doi]
• SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word EmbeddingsTodor Mihaylov, Preslav Nakov. 879-886 [doi]
• COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial HeadlinesKim Schouten, Flavius Frasincar, Franciska de Jong. 883-887 [doi]
• MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?Francisco Guzmán, Preslav Nakov, Lluís Màrquez. 887-895 [doi]
• ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial DomainMengxiao Jiang, Man Lan, Yuanbin Wu. 888-893 [doi]
• IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial TextAbhishek Kumar, Abhishek Sethi, Md. Shad Akhtar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya. 894-898 [doi]
• ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English ForaAlberto Barrón-Cedeño, Giovanni Da San Martino, Shafiq R. Joty, Alessandro Moschitti, Fahad Al-Obaidli, Salvatore Romeo, Kateryna Tymoshenko, Antonio Uva 0002. 896-903 [doi]
• IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment AnalysisDeepanway Ghosal, Shobhit Bhatnagar, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya. 899-903 [doi]
• FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word EmbeddingsPedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares, Eugénio C. Oliveira. 904-908 [doi]
• ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repositoryChang e Jia. 904-909 [doi]
• UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMRKhoa Nguyen, Dang Nguyen. 909-913 [doi]
• UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense TaggingSilvio Cordeiro, Carlos Ramisch, Aline Villavicencio. 910-917 [doi]
• Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented AttentionJan Buys, Phil Blunsom. 914-919 [doi]
• WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised ModelsXin Tang, Fei Li, Dong-Hong Ji. 918-924 [doi]
• FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducersSimon Mille, Roberto Carlini, Alicia Burga, Leo Wanner. 920-923 [doi]
• RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and GenerationNormunds Gruzitis, Didzis Gosko, Guntis Barzdins. 924-928 [doi]
• UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)Jari Björne, Tapio Salakoski. 925-930 [doi]
• The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic ParsingRik van Noord, Johan Bos. 929-933 [doi]
• UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random FieldsMohammad Javad Hosseini, Noah A. Smith, Su-In Lee. 931-936 [doi]
• PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External KnowledgeLiang Wang, Sujian Li. 934-937 [doi]
• ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word EmbeddingsAngelika Kirilin, Felix Krauss, Yannick Versley. 937-945 [doi]
• NTNU-1$@$ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random FieldsErwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik, Rune Sætre. 938-941 [doi]
• EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTIONSteffen Eger, Erik-Lân Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha, Iryna Gurevych. 942-946 [doi]
• VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)Andreas Scherbakov, Ekaterina Vylomova, Fei Liu 0023, Timothy Baldwin. 946-952 [doi]
• LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic NetworkIsabel Segura-Bedmar, Cristóbal Colón-Ruiz, Paloma Martínez. 947-950 [doi]
• The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random FieldsLung-Hao Lee, Kuei-Ching Lee, Yuen-Hsien Tseng. 951-955 [doi]
• PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word IdentificationKrzysztof Wrobel. 953-957 [doi]
• MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific PublicationsSijia Liu, Feichen Shen, Vipin Chaudhary, Hongfang Liu. 956-960 [doi]
• USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence PerplexityJosé Manuel Martínez Martínez, Liling Tan. 958-962 [doi]
• Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE AnnotatorRoman Kern, Stefan Falk, Andi Rexha. 961-964 [doi]
• Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble MessGillin Nat. 963-968 [doi]
• NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific DocumentsBiswanath Barik, Erwin Marsi. 965-968 [doi]
• LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural NetworkVíctor Suárez-Paniagua, Isabel Segura-Bedmar, Paloma Martínez. 969-972 [doi]
• SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System VotingGustavo Paetzold, Lucia Specia. 969-974 [doi]
• WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence LabelingAnimesh Prasad, Min-Yen Kan. 973-977 [doi]
• Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List FeaturesJulian Brooke, Alexandra L. Uitdenbogerd, Timothy Baldwin. 975-981 [doi]
• MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural NetworksJi Young Lee, Franck Dernoncourt, Peter Szolovits. 978-984 [doi]
• CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word IdentificationElnaz Davoodi, Leila Kosseim. 982-985 [doi]
• TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific PapersTomoki Tsujimura, Makoto Miwa, Yutaka Sasaki. 985-989 [doi]
• JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a SentenceNiloy Mukherjee, Braja Gopal Patra, Dipankar Das, Sivaji Bandyopadhyay. 986-990 [doi]
• SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature GenerationGábor Berend. 990-994 [doi]
• MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-ClassifierShervin Malmasi, Marcos Zampieri. 991-995 [doi]
• LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling ModelSimon Hernandez, Davide Buscaldi, Thierry Charnois. 995-999 [doi]
• LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier EnsemblesShervin Malmasi, Mark Dras, Marcos Zampieri. 996-1000 [doi]
• EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User DevelopmentMarek Kubis, Pawel Skórzewski, Tomasz Zietkiewicz. 1000-1004 [doi]
• MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord IdentificationMarcos Zampieri, Liling Tan, Josef van Genabith. 1001-1005 [doi]
• Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notesSarath P. R., Manikandan R, Yoshiki Niwa. 1005-1009 [doi]
• Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning ModelsPrafulla Choubey, Shubham Pateria. 1006-1010 [doi]
• NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptationPo-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang, Hsin-Hsi Chen. 1010-1013 [doi]
• TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic FeaturesFrancesco Ronzano, Ahmed AbuRa'ed, Luis Espinosa Anke, Horacio Saggion. 1011-1016 [doi]
• XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid ModelYu Long, Zhijing Li, Xuan Wang, Chen Li. 1014-1018 [doi]
• IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid ClassificationAshish Palakurthi, Radhika Mamidi. 1017-1021 [doi]
• ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and eventsAndre Lamurias, Diana Sousa, Sofia Pereira, Luka A. Clarke, Francisco M. Couto. 1019-1023 [doi]
• AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word EmbeddingSanjay S. P., Anand Kumar, K. P. Soman. 1022-1027 [doi]
• GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information ExtractionSean MacAvaney, Arman Cohan, Nazli Goharian. 1024-1029 [doi]
• CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks rightJoachim Bingel, Natalie Schluter, Héctor Martínez Alonso. 1028-1033 [doi]
• KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical RecordsArtuur Leeuwenberg, Marie-Francine Moens. 1030-1034 [doi]
• HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision TreesMaury Quijada, Julie Medero. 1034-1037 [doi]
• UWB at SemEval-2016 Task 11: Exploring Features for Complex Word IdentificationMichal Konkol. 1038-1041 [doi]
• AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word IdentificationOnur Kuru. 1042-1046 [doi]
• Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus FrequencyDavid Kauchak. 1047-1051 [doi]
• SemEval-2016 Task 8: Meaning Representation ParsingJonathan May. 1063-1073 [doi]
• SemEval-2016 Task 9: Chinese Semantic Dependency ParsingWanxiang Che, Yanqiu Shao, Ting Liu, Yu Ding. 1074-1080 [doi]
• SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)Georgeta Bordea, Els Lefever, Paul Buitelaar. 1081-1091 [doi]
• SemEval-2016 Task 14: Semantic Taxonomy EnrichmentDavid Jurgens, Mohammad Taher Pilehvar. 1092-1102 [doi]
• UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity MeasurementHua He, John Wieting, Kevin Gimpel, Jinfeng Rao, Jimmy J. Lin. 1103-1108 [doi]
• Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set ProgrammingMishal Kazmi, Peter Schüller. 1109-1115 [doi]
• KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and AnswersSimone Filice, Danilo Croce, Alessandro Moschitti, Roberto Basili. 1116-1123 [doi]
• SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant SupervisionJan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurélien Lucchi, Valeria De Luca, Martin Jaggi. 1124-1128 [doi]
• IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment AnalysisAyush Kumar 0003, Sarah Kohail, Amit Kumar, Asif Ekbal, Chris Biemann. 1129-1135 [doi]
• LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiersJulien Tourille, Olivier Ferret, Aurélie Névéol, Xavier Tannier. 1136-1142 [doi]
• RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing AccuracyGuntis Barzdins, Didzis Gosko. 1143-1147 [doi]
• DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics coreAlastair Butler. 1148-1153 [doi]
• M2L at SemEval-2016 Task 8: AMR Parsing with Neural NetworksYevgeniy Puzikov, Daisuke Kawahara, Sadao Kurohashi. 1154-1159 [doi]
• ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural NetworkLauritz Brandt, David Grimm, Mengfei Zhou, Yannick Versley. 1160-1166 [doi]
• UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-boundJames Goodman, Andreas Vlachos, Jason Naradowsky. 1167-1172 [doi]
• CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR ParserChuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji, Nianwen Xue. 1173-1178 [doi]
• The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with BoxerJohannes Bjerva, Johan Bos, Hessel Haagsma. 1179-1184 [doi]
• UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR ParsingXiaochang Peng, Daniel Gildea. 1185-1189 [doi]
• CLIP$@$UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to SearchSudha Rao, Yogarshi Vyas, Hal Daumé III, Philip Resnik. 1190-1196 [doi]
• CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural NetworksWilliam Foland, James H. Martin. 1197-1201 [doi]
• CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp LossJeffrey Flanigan, Chris Dyer, Noah A. Smith, Jaime G. Carbonell. 1202-1206 [doi]
• IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and BootstrappingArtsiom Artsymenia, Palina Dounar, Maria Yermakovich. 1207-1211 [doi]
• OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency ParserLifeng Jin, Manjuan Duan, William Schuler. 1212-1217 [doi]
• OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial GrammarManjuan Duan, Lifeng Jin, William Schuler. 1218-1224 [doi]
• LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressionsCyril Grouin, Véronique Moriceau. 1225-1230 [doi]
• Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical NotesSarath P. R., Manikandan R, Yoshiki Niwa. 1231-1236 [doi]
• CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniquesVeera Raghavendra Chikka. 1237-1240 [doi]
• VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEvalTommaso Caselli, Roser Morante. 1241-1247 [doi]
• GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical NarrativesArman Cohan, Kevin Meurer, Nazli Goharian. 1248-1255 [doi]
• UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical TextAbdulrahman Al Abdulsalam, Sumithra Velupillai, Stéphane Meystre. 1256-1262 [doi]
• ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEntMarcia Barros, Andre Lamurias, Gonçalo Figueiró, Marta Antunes, Joana Teixeira, Alexandre Pinheiro, Francisco M. Couto. 1263-1267 [doi]
• UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology ReportsPeng Li, Heng Huang. 1268-1273 [doi]
• Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information ExtractionJason Alan Fries. 1274-1279 [doi]
• KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical RecordsArtuur Leeuwenberg, Marie-Francine Moens. 1280-1285 [doi]
• CENTAL at SemEval-2016 Task 12: a linguistically fed CRF model for medical and temporal information extractionCharlotte Hansart, Damien De Meyere, Patrick Watrin, André Bittar, Cédrick Fairon. 1286-1291 [doi]
• UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical NotesHee-jin Lee, Hua Xu, Jingqi Wang, Yaoyun Zhang, Sungrim Moon, Jun Xu 0007, Yonghui Wu. 1292-1297 [doi]
• NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy ExtractionJoel Pocostales. 1298-1302 [doi]
• USAAR at SemEval-2016 Task 13: Hyponym EndocentricityLiling Tan, Francis Bond, Josef van Genabith. 1303-1309 [doi]
• JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym IdentificationPromita Maitra, Dipankar Das. 1310-1314 [doi]
• QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy AcquisitionGuillaume Cleuziou, Jose G. Moreno. 1315-1319 [doi]
• TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused CrawlingAlexander Panchenko, Stefano Faralli, Eugen Ruppert, Steffen Remus, Hubert Naets, Cédrick Fairon, Simone Paolo Ponzetto, Chris Biemann. 1320-1327 [doi]
• Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic TaxonomiesTed Pedersen. 1328-1331 [doi]
• TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based EmbeddingsLuis Espinosa Anke, Francesco Ronzano, Horacio Saggion. 1332-1336 [doi]
• MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence RankingMichael Schlichtkrull, Héctor Martínez Alonso. 1337-1341 [doi]
• Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectorsHristo Tanev, Agata Rotondi. 1342-1345 [doi]
• UMNDuluth at SemEval-2016 Task 14: WordNet's Missing LemmasJon Rusert, Ted Pedersen. 1346-1350 [doi]
• VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichmentBridget T. McInnes. 1351-1355 [doi]