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11th International Workshop on Semantic Evaluation (SemEval-2017)

WORKSHOP PROGRAM

3 August 2017

09:00–09:15Welcome / Opening Remarks
09:15–10:30Invited Talk: From Naive Physics to Connotation: Modeling Commonsense in Frame Semantics
Yejin Choi
10:30–11:00Coffee
11:00–12:30Task Descriptions
11:00–11:15SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio and Lucia Specia
11:15–11:30SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity
Jose Camacho-Collados, Mohammad Taher Pilehvar, Nigel Collier and Roberto Navigli
11:30–11:45SemEval-2017 Task 3: Community Question Answering
Preslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin and Karin Verspoor
11:45–12:00SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor
Peter Potash, Alexey Romanov and Anna Rumshisky
12:00–12:15SemEval-2017 Task 7: Detection and Interpretation of English Puns
Tristan Miller, Christian Hempelmann and Iryna Gurevych
12:15–12:30SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi and Arkaitz Zubiaga
12:30–14:00Lunch
14:00–15:30Best Of SemEval
14:00–14:15BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity
Hao Wu, Heyan Huang, Ping Jian, Yuhang Guo and Chao Su
14:15–14:30ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge
Robert Speer and Joanna Lowry-Duda
14:30–14:45IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification
Titas Nandi, Chris Biemann, Seid Muhie Yimam, Deepak Gupta, Sarah Kohail, Asif Ekbal and Pushpak Bhattacharyya
14:45–15:00HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition
David Donahue, Alexey Romanov and Anna Rumshisky
15:00–15:15Idiom Savant at Semeval-2017 Task 7: Detection and Interpretation of English Puns
Samuel Doogan, Aniruddha Ghosh, Hanyang Chen and Tony Veale
15:15–15:30Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
Elena Kochkina, Maria Liakata and Isabelle Augenstein
15:30–16:00Coffee
16:00–16:30Discussion
16:30–17:30Poster Session
16:30–17:30CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
Jérémy Ferrero, Laurent Besacier, Didier Schwab and Frédéric Agnès
16:30–17:30UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features
Hussein T. Al-Natsheh, Lucie Martinet, Fabrice Muhlenbach and Djamel Abdelkader ZIGHED
16:30–17:30DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
Nabin Maharjan, Rajendra Banjade, Dipesh Gautam, Lasang J. Tamang and Vasile Rus
16:30–17:30FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach
Basma Hassan, Samir AbdelRahman, Reem Bahgat and Ibrahim Farag
16:30–17:30HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity
Yang Shao
16:30–17:30LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting
El Moatez Billah NAGOUDI, Jérémy Ferrero and Didier Schwab
16:30–17:30OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity
Martyna Śpiewak, Piotr Sobecki and Daniel Karaś
16:30–17:30Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity
Cristina España-Bonet and Alberto Barrón-Cedeño
16:30–17:30QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings
Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Jinyong Cheng, Yuehan Du and Shuwang Han
16:30–17:30ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity
Johannes Bjerva and Robert Östling
16:30–17:30ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing
Wenjie Liu, Chengjie Sun, Lei Lin and Bingquan Liu
16:30–17:30Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
WenLi Zhuang and Ernie Chang
16:30–17:30SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector
Mirela-Stefania Duma and Wolfgang Menzel
16:30–17:30STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble
Sarah Kohail, Amr Rekaby Salama and Chris Biemann
16:30–17:30UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity
Joe Barrow and Denis Peskov
16:30–17:30MITRE at SemEval-2017 Task 1: Simple Semantic Similarity
John Henderson, Elizabeth Merkhofer, Laura Strickhart and Guido Zarrella
16:30–17:30ECNU 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 Similarity
Junfeng Tian, Zhiheng Zhou, Man Lan and Yuanbin Wu
16:30–17:30PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
I-Ta Lee, Mahak Goindani, Chang Li, Di Jin, Kristen Marie Johnson, Xiao Zhang, Maria Leonor Pacheco and Dan Goldwasser
16:30–17:30RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity
Ergun Biçici
16:30–17:30LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity
Ignacio Arroyo-Fernández and Ivan Vladimir Meza Ruiz
16:30–17:30L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity
Pedro Fialho, Hugo Patinho Rodrigues, Luísa Coheur and Paulo Quaresma
16:30–17:30HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity
Junqing He, Long Wu, Xuemin Zhao and Yonghong Yan
16:30–17:30Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts
Pablo Gamallo
16:30–17:30Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarity
Josué Melka and Gilles Bernard
16:30–17:30QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Ping Jian, Shumin Shi and Heyan Huang
16:30–17:30RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh’s-like list
Sergio Jimenez, George Dueñas, Lorena Gaitan and Jorge Segura
16:30–17:30MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approach
Enrico Mensa, Daniele P. Radicioni and Antonio Lieto
16:30–17:30HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment
Behrang QasemiZadeh and Laura Kallmeyer
16:30–17:30Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity
Niloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hosseini pour and Aryan Vahid pour
16:30–17:30Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia
Claudio Delli Bovi and Alessandro Raganato
16:30–17:30Wild Devs’ at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity
Răzvan-Gabriel Rotari, Ionut Hulub, Stefan Oprea, Mihaela Plamada-Onofrei, Alina Beatrice Lorent, Raluca Preisler, Adrian Iftene and Diana Trandabat
16:30–17:30TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
Mohammed R. H. Qwaider, Abed Alhakim Freihat and Fausto Giunchiglia
16:30–17:30UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic
Yassine El Adlouni, Imane Lahbari, Horacio Rodriguez, Mohammed Meknassi, Said Ouatik El Alaoui and Noureddine Ennahnahi
16:30–17:30Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering
Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li and Ming Zhou
16:30–17:30MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks
Miguel J. Rodrigues and Francisco M Couto
16:30–17:30EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering
Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang and Zhao Lu
16:30–17:30FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto and Ludovica Pannitto
16:30–17:30SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity
Le Qi, Yu Zhang and Ting Liu
16:30–17:30LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features
Naman Goyal
16:30–17:30SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering
Delphine Charlet and Geraldine Damnati
16:30–17:30FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering
Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li and Zhaoyun Ding
16:30–17:30KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering
Simone Filice, Giovanni Da San Martino and Alessandro Moschitti
16:30–17:30SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering
Jan Milan Deriu and Mark Cieliebak
16:30–17:30TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA
Filip Šaina, Toni Kukurin, Lukrecija Puljić, Mladen Karan and Jan Šnajder
16:30–17:30GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora
Nada Almarwani and Mona Diab
16:30–17:30NLM_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering
Asma Ben Abacha and Dina Demner-Fushman
16:30–17:30bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features
Yuta Koreeda, Takuya Hashito, Yoshiki Niwa, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi and Kohsuke Yanai
16:30–17:30QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums
Marwan Torki, Maram Hasanain and Tamer Elsayed
16:30–17:30ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task
Guoshun Wu, Yixuan Sheng, Man Lan and Yuanbin Wu
16:30–17:30UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA
Surya Agustian and Hiroya Takamura
16:30–17:30Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection
Byron Galbraith, Bhanu Pratap and Daniel Shank
16:30–17:30QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter
Xiwu Han and Gregory Toner
16:30–17:30Duluth at SemEval-2017 Task 6: Language Models in Humor Detection
Xinru Yan and Ted Pedersen
16:30–17:30DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison
Christos Baziotis, Nikos Pelekis and Christos Doulkeridis
16:30–17:30TakeLab at SemEval-2017 Task 6: #RankingHumorIn4Pages
Marin Kukovačec, Juraj Malenica, Ivan Mršić, Antonio Šajatović, Domagoj Alagić and Jan Šnajder
16:30–17:30SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition
Andrew Cattle and Xiaojuan Ma
16:30–17:30#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets
Iuliana Alexandra Fleşcan-Lovin-Arseni, Ramona Andreea Turcu, Cristina Sirbu, Larisa Alexa, Sandra Maria Amarandei, Nichita Herciu, Constantin Scutaru, Diana Trandabat and Adrian Iftene
16:30–17:30SVNIT @ SemEval 2017 Task-6: Learning a Sense of Humor Using Supervised Approach
Rutal Mahajan and Mukesh Zaveri
16:30–17:30Duluth at SemEval-2017 Task 7 : Puns Upon a Midnight Dreary, Lexical Semantics for the Weak and Weary
Ted Pedersen
16:30–17:30UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics
Olga Vechtomova
16:30–17:30PunFields at SemEval-2017 Task 7: Employing Roget’s Thesaurus in Automatic Pun Recognition and Interpretation
Elena Mikhalkova and Yuri Karyakin
16:30–17:30JU CSE NLP @ SemEval 2017 Task 7: Employing Rules to Detect and Interpret English Puns
Aniket Pramanick and Dipankar Das
16:30–17:30N-Hance at SemEval-2017 Task 7: A Computational Approach using Word Association for Puns
Özge Sevgili, Nima Ghotbi and Selma Tekir
16:30–17:30ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation
Lluís-F. Hurtado, Encarna Segarra, Ferran Pla, Pascual Carrasco and José-Ángel González
16:30–17:30BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings
Dieke Oele and Kilian Evang
16:30–17:30UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns
Ankit Vadehra
16:30–17:30ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns
Yuhuan Xiu, Man Lan and Yuanbin Wu
16:30–17:30Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language
Vijayasaradhi Indurthi and Subba Reddy Oota
16:30–17:30UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
Hareesh Bahuleyan and Olga Vechtomova
16:30–17:30IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification
Yi-Chin Chen, Zhao-Yang Liu and Hung-Yu Kao
16:30–17:30NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter.
Omar Enayet and Samhaa R. El-Beltagy
16:30–17:30Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
Elena Kochkina, Maria Liakata and Isabelle Augenstein
16:30–17:30Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules
Marianela García Lozano, Hanna Lilja, Edward Tjörnhammar and Maja Karasalo
16:30–17:30DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics
Ankit Srivastava, Georg Rehm and Julian Moreno Schneider
16:30–17:30ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Feixiang Wang, Man Lan and Yuanbin Wu
16:30–17:30IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation
Vikram Singh, Sunny Narayan, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya

4 Aug 2017

09:00–09:30SemEval 2018 Tasks
09:30–10:30State of SemEval Discussion
10:30–11:00Coffee
11:00–12:30Task Descriptions
11:00–11:15SemEval-2017 Task 4: Sentiment Analysis in Twitter
Sara Rosenthal, Noura Farra and Preslav Nakov
11:15–11:30SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Keith Cortis, André Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh and Brian Davis
11:30–11:45SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation
Jonathan May and Jay Priyadarshi
11:45–12:00SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman and Andrew McCallum
12:00–12:15SemEval-2017 Task 11: End-User Development using Natural Language
Juliano Sales, Siegfried Handschuh and André Freitas
12:15–12:30SemEval-2017 Task 12: Clinical TempEval
Steven Bethard, Guergana Savova, Martha Palmer and James Pustejovsky
12:30–14:00Lunch
14:00–15:30Best Of SemEval
14:00–14:15BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
Mathieu Cliche
14:15–14:30Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines
Andrew Moore and Paul Rayson
14:30–14:45Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
Gerasimos Lampouras and Andreas Vlachos
14:45–15:00The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Waleed Ammar, Matthew Peters, Chandra Bhagavatula and Russell Power
15:00–15:15LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives
Julien Tourille, Olivier Ferret, Xavier Tannier and Aurélie Névéol
15:30–16:00Coffee
16:00–16:30Discussion
16:30–17:30Poster Session
16:30–17:30OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model
Ramy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El-Khoury, Ahmad Al Sallab, Hazem Hajj, Nizar Habash, Khaled Shaban and Wassim El-Hajj
16:30–17:30NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
Edilson Anselmo Corrêa Júnior, Vanessa Queiroz Marinho and Leandro Borges dos Santos
16:30–17:30deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter
Tzu-Hsuan Yang, Tzu-Hsuan Tseng and Chia-Ping Chen
16:30–17:30NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings
Yichun Yin, Yangqiu Song and Ming Zhang
16:30–17:30CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification
Raj Kumar Gupta and Yinping Yang
16:30–17:30SINAI at SemEval-2017 Task 4: User based classification
Salud María Jiménez-Zafra, Arturo Montejo-Ráez, Maite Martin and L. Alfonso Urena Lopez
16:30–17:30HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors
Abeed Sarker and Graciela Gonzalez
16:30–17:30ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter
Enkhzol Dovdon and José Saias
16:30–17:30SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification
Raphael Troncy, Enrico Palumbo, Efstratios Sygkounas and Giuseppe Rizzo
16:30–17:30Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter
Alon Rozental and Daniel Fleischer
16:30–17:30TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier
Naveen Kumar Laskari and Suresh Kumar Sanampudi
16:30–17:30Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets
Hala Mulki, Hatem Haddad, Mourad Gridach and Ismail Babaoğlu
16:30–17:30OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields
Chukwuyem Onyibe and Nizar Habash
16:30–17:30Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Nikolaos Malandrakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan and Alexandros Potamianos
16:30–17:30NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture
Nikolay Karpov
16:30–17:30MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification
Jingjing Zhao, Yan Yang and Bing Xu
16:30–17:30SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features
Mohammed Jabreel and Antonio Moreno
16:30–17:30Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
Hussam Hamdan
16:30–17:30DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis
Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis and Avi Arampatzis
16:30–17:30SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier
Angel Deborah S, S Milton Rajendram and T T Mirnalinee
16:30–17:30YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter
Ming Wang, Biao Chu, Qingxun Liu and Xiaobing Zhou
16:30–17:30LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification
Amal Htait, Sébastien Fournier and Patrice Bellot
16:30–17:30ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning
José-Ángel González, Ferran Pla and Lluís-F. Hurtado
16:30–17:30XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter
Yazhou Hao, YangYang Lan, Yufei Li and Chen Li
16:30–17:30Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention
Joosung Yoon, Kigon Lyu and Hyeoncheol Kim
16:30–17:30EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification
wang maoquan, Chen Shiyun, Xie yufei and Zhao lu
16:30–17:30funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang and Sameena Shah
16:30–17:30DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
Christos Baziotis, Nikos Pelekis and Christos Doulkeridis
16:30–17:30TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification
Georgios Balikas
16:30–17:30LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification
Mickael Rouvier
16:30–17:30TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision
Simon Müller, Tobias Huonder, Jan Deriu and Mark Cieliebak
16:30–17:30INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis
Sabino Miranda-Jiménez, Mario Graff, Eric Sadit Tellez and Daniela Moctezuma
16:30–17:30BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches
Deger Ayata, Murat Saraclar and Arzucan Ozgur
16:30–17:30TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter
David Lozić, Doria Šarić, Ivan Tokić, Zoran Medić and Jan Šnajder
16:30–17:30NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis
Samhaa R. El-Beltagy, Mona El kalamawy and Abu Bakr Soliman
16:30–17:30YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification
Haowei Zhang, Jin Wang, Jixian Zhang and Xuejie Zhang
16:30–17:30TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis
Amit Ajit Deshmane and Jasper Friedrichs
16:30–17:30UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
José Abreu, Iván Castro, Claudia Martínez, Sebastián Oliva and Yoan Gutiérrez
16:30–17:30ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
Yunxiao Zhou, Man Lan and Yuanbin Wu
16:30–17:30Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini and Jacopo Staiano
16:30–17:30SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
Angel Deborah S, S Milton Rajendram and T T Mirnalinee
16:30–17:30IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Zarmeen Nasim
16:30–17:30HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods
Tobias Cabanski, Julia Romberg and Stefan Conrad
16:30–17:30INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines
Tiago Zini, Karin Becker and Marcelo Dias
16:30–17:30HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks
Lidia Pivovarova, Llorenç Escoter, Arto Klami and Roman Yangarber
16:30–17:30NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Chung-Chi Chen, Hen-Hsen Huang and Hsin-Hsi Chen
16:30–17:30funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang and Xiaomo Liu
16:30–17:30SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
Narges Tabari, Armin Seyeditabari and Wlodek Zadrozny
16:30–17:30DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles
Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis and Avi Arampatzis
16:30–17:30TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Leon Rotim, Martin Tutek and Jan Šnajder
16:30–17:30UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation
Vineet John and Olga Vechtomova
16:30–17:30RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
Sudipta Kar, Suraj Maharjan and Thamar Solorio
16:30–17:30COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines
Kim Schouten, Flavius Frasincar and Franciska de Jong
16:30–17:30ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
Mengxiao Jiang, Man Lan and Yuanbin Wu
16:30–17:30IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text
Abhishek Kumar, Abhishek Sethi, Md Shad Akhtar, Asif Ekbal, Chris Biemann and Pushpak Bhattacharyya
16:30–17:30IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis
Deepanway Ghosal, Shobhit Bhatnagar, Md Shad Akhtar, Asif Ekbal and Pushpak Bhattacharyya
16:30–17:30FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
Pedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares and Eugénio Oliveira
16:30–17:30UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR
Khoa Nguyen and Dang Nguyen
16:30–17:30Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention
Jan Buys and Phil Blunsom
16:30–17:30FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers
Simon Mille, Roberto Carlini, Alicia Burga and Leo Wanner
16:30–17:30RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
Normunds Gruzitis, Didzis Gosko and Guntis Barzdins
16:30–17:30The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
Rik van Noord and Johan Bos
16:30–17:30PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge
Liang Wang and Sujian Li
16:30–17:30NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields
Erwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik and Rune Sætre
16:30–17:30EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
Steffen Eger, Erik-Lân Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha and Iryna Gurevych
16:30–17:30LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network
Isabel Segura-Bedmar, Cristóbal Colón-Ruiz and Paloma Martínez
16:30–17:30The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields
Lung-Hao Lee, Kuei-Ching Lee and Yuen-Hsien Tseng
16:30–17:30MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications
Sijia Liu, Feichen Shen, Vipin Chaudhary and Hongfang Liu
16:30–17:30Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
Roman Kern, Stefan Falk and Andi Rexha
16:30–17:30NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents
Biswanath Barik and Erwin Marsi
16:30–17:30LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network
Víctor Suárez-Paniagua, Isabel Segura-Bedmar and Paloma Martínez
16:30–17:30WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling
Animesh Prasad and Min-Yen Kan
16:30–17:30MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Ji Young Lee, Franck Dernoncourt and Peter Szolovits
16:30–17:30TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers
Tomoki Tsujimura, Makoto Miwa and Yutaka Sasaki
16:30–17:30SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation
Gábor Berend
16:30–17:30LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model
Simon David Hernandez, Davide Buscaldi and Thierry Charnois
16:30–17:30EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User Development
Marek Kubis, Paweł Skórzewski and Tomasz Zietkiewicz
16:30–17:30Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes
Sarath P R, Manikandan R and Yoshiki Niwa
16:30–17:30NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation
Po-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang and Hsin-Hsi Chen
16:30–17:30XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model
Yu Long, Zhijing Li, Xuan Wang and Chen Li
16:30–17:30ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and events
Andre Lamurias, Diana Sousa, Sofia Pereira, Luka Clarke and Francisco M Couto
16:30–17:30GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction
Sean MacAvaney, Arman Cohan and Nazli Goharian
16:30–17:30KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records
Artuur Leeuwenberg and Marie-Francine Moens