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ORCIDSemEval@ACL 2017: Vancouver, Canada
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel M. Cer, David Jurgens:
Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval@ACL 2017, Vancouver, Canada, August 3-4, 2017. Association for Computational Linguistics 2017 - Daniel M. Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, Lucia Specia:
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation. 1-14 - José Camacho-Collados, Mohammad Taher Pilehvar, Nigel Collier, Roberto Navigli:
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity. 15-26 - Preslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin, Karin Verspoor
:
SemEval-2017 Task 3: Community Question Answering. 27-48 - Peter Potash, Alexey Romanov, Anna Rumshisky:
SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor. 49-57 - Tristan Miller, Christian Hempelmann, Iryna Gurevych:
SemEval-2017 Task 7: Detection and Interpretation of English Puns. 58-68 - Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, Arkaitz Zubiaga:
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours. 69-76 - Hao Wu, Heyan Huang, Ping Jian, Yuhang Guo, Chao Su:
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity. 77-84 - Robyn Speer, Joanna Lowry-Duda:
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge. 85-89 - Titas Nandi, Chris Biemann, Seid Muhie Yimam, Deepak Gupta, Sarah Kohail, Asif Ekbal, Pushpak Bhattacharyya:
IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification. 90-97 - David Donahue, Alexey Romanov, Anna Rumshisky:
HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition. 98-102 - Samuel Doogan, Aniruddha Ghosh, Hanyang Chen, Tony Veale:
Idiom Savant at Semeval-2017 Task 7: Detection and Interpretation of English Puns. 103-108 - Jérémy Ferrero, Laurent Besacier, Didier Schwab, Frédéric Agnès:
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity. 109-114 - Hussein T. Al-Natsheh, Lucie Martinet, Fabrice Muhlenbach, Djamel Abdelkader Zighed:
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features. 115-119 - Nabin Maharjan, Rajendra Banjade, Dipesh Gautam, Lasang Jimba Tamang, Vasile Rus:
DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output. 120-124 - Basma Hassan, Samir E. AbdelRahman, Reem Bahgat, Ibrahim Farag:
FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach. 125-129 - Yang Shao:
HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity. 130-133 - El Moatez Billah Nagoudi, Jérémy Ferrero, Didier Schwab:
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting. 134-138 - Martyna Spiewak, Piotr Sobecki, Daniel Karas:
OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity. 139-143 - Cristina España-Bonet, Alberto Barrón-Cedeño:
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity. 144-149 - Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Jinyong Cheng, Yuehan Du, Shuwang Han:
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings. 150-153 - Johannes Bjerva, Robert Östling:
ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity. 154-158 - Wenjie Liu, Chengjie Sun, Lei Lin, Bingquan Liu:
ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing. 159-163 - Wenli Zhuang, Ernie Chang:
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model. 164-169 - Mirela-Stefania Duma, Wolfgang Menzel:
SEF$@$UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector. 170-174 - Sarah Kohail, Amr Rekaby Salama, Chris Biemann:
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble. 175-179 - Joe Barrow, Denis Peskov:
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity. 180-184 - John C. Henderson, Elizabeth M. Merkhofer, Laura Strickhart, Guido Zarrella:
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity. 185-190 - Junfeng Tian, Zhiheng Zhou, Man Lan, Yuanbin Wu:
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 Similarity. 191-197 - I-Ta Lee, Mahak Goindani, Chang Li, Di Jin, Kristen Johnson, Xiao Zhang, Maria Leonor Pacheco, Dan Goldwasser:
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings. 198-202 - Ergun Biçici:
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity. 203-207 - Ignacio Arroyo-Fernández, Iván Vladimir Meza Ruíz:
LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity. 208-212 - Pedro Fialho, Hugo Rodrigues, Luísa Coheur, Paulo Quaresma:
L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity. 213-219 - Junqing He, Long Wu, Xuemin Zhao, Yonghong Yan:
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity. 220-225 - Pablo Gamallo:
Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts. 226-229 - Josué Melka, Gilles Bernard:
Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarity. 230-234 - Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Ping Jian, Shumin Shi, Heyan Huang:
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base. 235-238 - Sergio Jiménez, George Dueñas, Lorena Gaitan, Jorge Segura:
RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list. 239-244 - Enrico Mensa, Daniele Paolo Radicioni, Antonio Lieto:
MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approach. 245-249 - Behrang QasemiZadeh, Laura Kallmeyer:
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment. 250-255 - Niloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hosseini pour, Aryan Vahid pour:
Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity. 256-260 - Claudio Delli Bovi, Alessandro Raganato:
Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia. 261-266 - Razvan-Gabriel Rotari, Ionut Hulub, Stefan Oprea, Mihaela Plamada-Onofrei, Alina Beatrice Lorent, Raluca Preisler, Adrian Iftene, Diana Trandabat:
Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity. 267-270 - Mohammed R. H. Qwaider, Abed Alhakim Freihat, Fausto Giunchiglia:
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers. 271-274 - Yassine El Adlouni, Imane Lahbari, Horacio Rodríguez, Mohammed Meknassi, Said Ouatik El Alaoui, Noureddine Ennahnahi:
UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic. 275-279 - Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou:
Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering. 280-286 - Miguel J. Rodrigues, Francisco M. Couto:
MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks. 287-291 - Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, Zhao Lu:
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering. 292-298 - Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto:
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. 299-304 - Le Qi, Yu Zhang, Ting Liu:
SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity. 305-309 - Naman Goyal:
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features. 310-314 - Delphine Charlet, Géraldine Damnati:
SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering. 315-319 - Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, Zhaoyun Ding:
FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering. 320-325 - Simone Filice, Giovanni Da San Martino, Alessandro Moschitti:
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering. 326-333 - Jan Deriu, Mark Cieliebak:
SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering. 334-338 - Filip Saina, Toni Kukurin, Lukrecija Puljic, Mladen Karan, Jan Snajder:
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA. 339-343 - Nada AlMarwani, Mona T. Diab:
GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora. 344-348 - Asma Ben Abacha, Dina Demner-Fushman:
NLM_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering. 349-352 - Yuta Koreeda, Takuya Hashito, Yoshiki Niwa, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi, Kohsuke Yanai:
bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features. 353-359 - Marwan Torki, Maram Hasanain, Tamer Elsayed:
QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums. 360-364 - GuoShun Wu, Yixuan Sheng, Man Lan, Yuanbin Wu:
ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task. 365-369 - Surya Agustian, Hiroya Takamura:
UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA. 370-374 - Byron Galbraith, Bhanu Pratap, Daniel Shank:
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection. 375-379 - Xiwu Han, Gregory Toner:
QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter. 380-384 - Xinru Yan, Ted Pedersen:
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection. 385-389 - Christos Baziotis, Nikos Pelekis, Christos Doulkeridis:
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison. 390-395 - Marin Kukovacec, Juraj Malenica, Ivan Mrsic, Antonio Sajatovic, Domagoj Alagic, Jan Snajder:
TakeLab at SemEval-2017 Task 6: #RankingHumorIn4Pages. 396-400 - Andrew Cattle, Xiaojuan Ma:
SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition. 401-406 - Iuliana Alexandra Flescan-Lovin-Arseni, Ramona Andreea Turcu, Cristina Sîrbu, Larisa Alexa, Sandra Maria Amarandei, Nichita Herciu, Constantin Scutaru, Diana Trandabat, Adrian Iftene:
#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets. 407-410 - Rutal Mahajan, Mukesh A. Zaveri:
SVNIT $@$ SemEval 2017 Task-6: Learning a Sense of Humor Using Supervised Approach. 411-415 - Ted Pedersen:
Duluth at SemEval-2017 Task 7 : Puns Upon a Midnight Dreary, Lexical Semantics for the Weak and Weary. 416-420 - Olga Vechtomova:
UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics. 421-425 - Elena Mikhalkova, Yuri Karyakin:
PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in Automatic Pun Recognition and Interpretation. 426-431 - Aniket Pramanick, Dipankar Das:
JU CSE NLP $@$ SemEval 2017 Task 7: Employing Rules to Detect and Interpret English Puns. 432-435 - Özge Sevgili, Nima Ghotbi, Selma Tekir:
N-Hance at SemEval-2017 Task 7: A Computational Approach using Word Association for Puns. 436-439 - Lluís-F. Hurtado, Encarna Segarra, Ferran Pla, Pascual Carrasco, José-Ángel González:
ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation. 440-443 - Dieke Oele, Kilian Evang:
BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings. 444-448 - Ankit Vadehra:
UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns. 449-452 - Yuhuan Xiu, Man Lan, Yuanbin Wu:
ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns. 453-456 - Vijayasaradhi Indurthi, Subba Reddy Oota:
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language. 457-460 - Hareesh Bahuleyan, Olga Vechtomova:
UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features. 461-464 - Yi-Chin Chen, Zhao-Yang Liu, Hung-Yu Kao:
IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification. 465-469 - Omar Enayet, Samhaa R. El-Beltagy:
NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter. 470-474 - Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM. 475-480
- Marianela García Lozano, Hanna Lilja, Edward Tjörnhammar, Maja Karasalo:
Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules. 481-485 - Ankit Srivastava, Georg Rehm, Julián Moreno Schneider:
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics. 486-490 - Feixiang Wang, Man Lan, Yuanbin Wu:
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models. 491-496 - Vikram Singh, Sunny Narayan, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya:
IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation. 497-501 - Sara Rosenthal, Noura Farra, Preslav Nakov:
SemEval-2017 Task 4: Sentiment Analysis in Twitter. 502-518 - Keith Cortis, André Freitas, Tobias Daudert, Manuela Hürlimann, Manel Zarrouk, Siegfried Handschuh, Brian Davis:
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. 519-535 - Jonathan May, Jay Priyadarshi:
SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation. 536-545 - Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum:
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications. 546-555 - Juliano Efson Sales, Siegfried Handschuh, André Freitas:
SemEval-2017 Task 11: End-User Development using Natural Language. 556-564 - Steven Bethard, Guergana Savova, Martha Palmer, James Pustejovsky:
SemEval-2017 Task 12: Clinical TempEval. 565-572 - Mathieu Cliche:
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs. 573-580 - Andrew Moore, Paul Rayson:
Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines. 581-585 - Gerasimos Lampouras, Andreas Vlachos:
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR. 586-591 - Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, Russell Power:
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction. 592-596 - Julien Tourille, Olivier Ferret, Xavier Tannier, Aurélie Névéol:
LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives. 597-602 - Ramy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El Khoury, Ahmad Al Sallab, Hazem M. Hajj, Nizar Habash, Khaled Bashir Shaban, Wassim El-Hajj:
OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model. 603-610 - Edilson Anselmo Corrêa Júnior, Vanessa Queiroz Marinho, Leandro Borges dos Santos:
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis. 611-615 - Tzu-Hsuan Yang, Tzu-Hsuan Tseng, Chia-Ping Chen:
deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter. 616-620 - Yichun Yin, Yangqiu Song, Ming Zhang:
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings. 621-625 - Raj Kumar Gupta, Yinping Yang:
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification. 626-633 - Salud María Jiménez-Zafra, Arturo Montejo-Ráez, María Teresa Martín-Valdivia, Luis Alfonso Ureña López:
SINAI at SemEval-2017 Task 4: User based classification. 634-639 - Abeed Sarker, Graciela Gonzalez:
HLP$@$UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors. 640-643 - Enkhzol Dovdon, José Saias:
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter. 644-647 - Raphaël Troncy, Enrico Palumbo, Efstratios Sygkounas, Giuseppe Rizzo:
SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification. 648-652 - Alon Rozental, Daniel Fleischer:
Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter. 653-658 - Naveen Kumar Laskari, Suresh Kumar Sanampudi:
TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier. 659-663 - Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu:
Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets. 664-669 - Chukwuyem Onyibe, Nizar Habash:
OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields. 670-674 - Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Nikolaos Malandrakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth S. Narayanan, Alexandros Potamianos:
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter. 675-682 - Nikolay Karpov:
NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture. 683-688 - Jingjing Zhao, Yan Yang, Bing Xu:
MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification. 689-693 - Mohammed Jabreel, Antonio Moreno:
SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features. 694-699 - Hussam Hamdan:
Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification. 700-703 - Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis:
DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis. 704-708 - Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee:
SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier. 709-712 - Ming Wang, Biao Chu, Qingxun Liu, Xiaobing Zhou:
YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter. 713-717 - Amal Htait, Sébastien Fournier, Patrice Bellot:
LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification. 718-722 - José-Ángel González, Ferran Pla, Lluís-F. Hurtado:
ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning. 723-727 - Yazhou Hao, YangYang Lan, Yufei Li, Chen Li:
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter. 728-731 - Joosung Yoon, Kigon Lyu, Hyeoncheol Kim:
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention. 732-736 - Maoquan Wang, Shiyun Chen, Xie Yufei, Zhao Lu:
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification. 737-740 - Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang, Sameena Shah:
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts. 741-746 - Christos Baziotis, Nikos Pelekis, Christos Doulkeridis:
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis. 747-754 - Georgios Balikas:
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification. 755-759 - Mickael Rouvier:
LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification. 760-765 - Simon Müller, Tobias Huonder, Jan Deriu, Mark Cieliebak:
TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision. 766-770 - Sabino Miranda-Jiménez, Mario Graff, Eric Sadit Tellez, Daniela Moctezuma:
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis. 771-776 - Deger Ayata, Murat Saraclar, Arzucan Özgür:
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches. 777-783 - David Lozic, Doria Saric, Ivan Tokic, Zoran Medic, Jan Snajder:
TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter. 784-789 - Samhaa R. El-Beltagy, Mona El Kalamawy, Abu Bakr Soliman:
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis. 790-795 - Haowei Zhang, Jin Wang, Jixian Zhang, Xuejie Zhang:
YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification. 796-801 - Amit Ajit Deshmane, Jasper Friedrichs:
TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis. 802-806 - José Ignacio Abreu, Iván Castro, Claudia Martínez, Sebastián Oliva, Yoan Gutiérrez:
UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter. 807-811 - Yunxiao Zhou, Man Lan, Yuanbin Wu:
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. 812-816 - Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini, Jacopo Staiano:
Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines. 817-822 - Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee:
SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model. 823-826 - Zarmeen Nasim:
IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. 827-831 - Tobias Cabanski, Julia Romberg, Stefan Conrad:
HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods. 832-836 - Tiago Zini, Karin Becker, Marcelo Dias:
INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines. 837-841 - Lidia Pivovarova, Llorenç Escoter, Arto Klami, Roman Yangarber:
HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks. 842-846 - Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen:
NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. 847-851 - Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang, Xiaomo Liu:
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter. 852-856 - Narges Tabari, Armin Seyeditabari, Wlodek Zadrozny:
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets. 857-860 - Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, Avi Arampatzis:
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles. 861-865 - Leon Rotim, Martin Tutek, Jan Snajder:
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news. 866-871 - Vineet John, Olga Vechtomova:
UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation. 872-876 - Sudipta Kar, Suraj Maharjan, Thamar Solorio:
RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks. 877-882 - Kim Schouten, Flavius Frasincar, Franciska de Jong:
COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines. 883-887 - Mengxiao Jiang, Man Lan, Yuanbin Wu:
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain. 888-893 - Abhishek Kumar, Abhishek Sethi, Md. Shad Akhtar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya:
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text. 894-898 - Deepanway Ghosal, Shobhit Bhatnagar, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya:
IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis. 899-903 - Pedro Saleiro, Eduarda Mendes Rodrigues, Carlos Soares, Eugénio C. Oliveira:
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings. 904-908 - Khoa Nguyen, Dang Nguyen:
UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR. 909-913 - Jan Buys, Phil Blunsom:
Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention. 914-919 - Simon Mille, Roberto Carlini, Alicia Burga, Leo Wanner:
FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers. 920-923 - Normunds Gruzitis, Didzis Gosko, Guntis Barzdins:
RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation. 924-928 - Rik van Noord, Johan Bos:
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing. 929-933 - Liang Wang, Sujian Li:
PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge. 934-937 - Erwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik, Rune Sætre:
NTNU-1$@$ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields. 938-941 - Steffen Eger, Erik-Lân Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha, Iryna Gurevych:
EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION. 942-946 - Isabel Segura-Bedmar, Cristóbal Colón-Ruiz, Paloma Martínez:
LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network. 947-950 - Lung-Hao Lee, Kuei-Ching Lee, Yuen-Hsien Tseng:
The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields. 951-955 - Sijia Liu, Feichen Shen, Vipin Chaudhary, Hongfang Liu:
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications. 956-960 - Roman Kern, Stefan Falk, Andi Rexha:
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator. 961-964 - Biswanath Barik, Erwin Marsi:
NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents. 965-968 - Víctor Suárez-Paniagua, Isabel Segura-Bedmar, Paloma Martínez:
LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network. 969-972 - Animesh Prasad, Min-Yen Kan:
WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling. 973-977 - Ji Young Lee, Franck Dernoncourt, Peter Szolovits:
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks. 978-984 - Tomoki Tsujimura, Makoto Miwa, Yutaka Sasaki:
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers. 985-989 - Gábor Berend:
SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation. 990-994 - Simon David Hernandez, Davide Buscaldi, Thierry Charnois:
LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model. 995-999 - Marek Kubis, Pawel Skórzewski, Tomasz Zietkiewicz:
EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User Development. 1000-1004 - Sarath P. R., Manikandan R, Yoshiki Niwa:
Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes. 1005-1009 - Po-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang, Hsin-Hsi Chen:
NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation. 1010-1013 - Yu Long, Zhijing Li, Xuan Wang, Chen Li:
XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model. 1014-1018 - Andre Lamurias, Diana F. Sousa, Sofia Pereira, Luka A. Clarke, Francisco M. Couto:
ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and events. 1019-1023 - Sean MacAvaney, Arman Cohan, Nazli Goharian:
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction. 1024-1029 - Artuur Leeuwenberg, Marie-Francine Moens:
KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records. 1030-1034
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