Abstract is missing.
- SemEval-2018 Task 1: Affect in TweetsSaif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko. 1-17 [doi]
- SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in TweetsVenkatesh Duppada, Royal Jain, Sushant Hiray. 18-23 [doi]
- SemEval 2018 Task 2: Multilingual Emoji PredictionFrancesco Barbieri, José Camacho-Collados, Francesco Ronzano, Luis Espinosa Anke, Miguel Ballesteros, Valerio Basile, Viviana Patti, Horacio Saggion. 24-33 [doi]
- Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji PredictionÇagri Çöltekin, Taraka Rama. 34-38 [doi]
- SemEval-2018 Task 3: Irony Detection in English TweetsCynthia Van Hee, Els Lefever, Véronique Hoste. 39-50 [doi]
- THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task LearningChuhan Wu, Fangzhao Wu, Sixing Wu, Junxin Liu, Zhigang Yuan, Yongfeng Huang. 51-56 [doi]
- SemEval 2018 Task 4: Character Identification on Multiparty DialoguesJinho D. Choi, Henry Y. Chen. 57-64 [doi]
- AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity LibraryLaura Aina, Carina Silberer, Ionut-Teodor Sorodoc, Matthijs Westera, Gemma Boleda. 65-69 [doi]
- SemEval-2018 Task 5: Counting Events and Participants in the Long TailMarten Postma, Filip Ilievski, Piek Vossen. 70-80 [doi]
- KOI at SemEval-2018 Task 5: Building Knowledge Graph of IncidentsParamita Mirza, Fariz Darari, Rahmad Mahendra. 81-87 [doi]
- SemEval 2018 Task 6: Parsing Time NormalizationsEgoitz Laparra, Dongfang Xu, Ahmed ElSayed, Steven Bethard, Martha Palmer. 88-96 [doi]
- Chrono at SemEval-2018 Task 6: A System for Normalizing Temporal ExpressionsAmy L. Olex, Luke Maffey, Nicholas Morgan, Bridget T. McInnes. 97-101 [doi]
- NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog TextMauro Dragoni. 102-108 [doi]
- FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect AnalysisMaja Karasalo, Mattias Nilsson, Magnus Rosell, Ulrika Wickenberg-Bolin. 109-115 [doi]
- NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity DeterminationZhengxin Zhang, Qimin Zhou, Hao Wu 0010. 116-122 [doi]
- LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweetsLuna De Bruyne, Orphée De Clercq, Véronique Hoste. 123-127 [doi]
- SINAI at SemEval-2018 Task 1: Emotion Recognition in TweetsFlor Miriam Plaza del Arco, Salud María Jiménez Zafra, Maite Martín, Luis Alfonso Ureña López. 128-132 [doi]
- UWB at SemEval-2018 Task 1: Emotion Intensity Detection in TweetsPavel Pribán, Tomás Hercig, Ladislav Lenc. 133-140 [doi]
- AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion ClassificationYanghoon Kim, Hwanhee Lee, Kyomin Jung. 141-145 [doi]
- INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment AnalysisMario Graff, Sabino Miranda-Jiménez, Eric Sadit Tellez, Daniela Moctezuma. 146-150 [doi]
- Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning ApproachGuillaume Daval-Frerot, Abdesselam Bouchekif, Anatole Moreau. 151-155 [doi]
- KDE-AFFECT at SemEval-2018 Task 1: Estimation of Affects in Tweet by Using Convolutional Neural Network for n-gramMasaki Aono, Shinnosuke Himeno. 156-161 [doi]
- RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification ProblemAysu Ezen-Can, Ethem F. Can. 162-166 [doi]
- Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion ClassificationHala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babaoglu. 167-171 [doi]
- DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and EmbeddingsDmitry Kravchenko, Lidia Pivovarova. 172-176 [doi]
- EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #TweetsRamona Andreea Turcu, Sandra Maria Amarandei, Iuliana Alexandra Flescan-Lovin-Arseni, Daniela Gîfu, Diana Trandabat. 177-180 [doi]
- uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based NetworkAhmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, David Van Bruwaene. 181-185 [doi]
- THU_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTMChuhan Wu, Fangzhao Wu, Junxin Liu, Zhigang Yuan, Sixing Wu, Yongfeng Huang. 186-192 [doi]
- EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of TweetsMohammed Jabreel, Antonio Moreno. 193-199 [doi]
- CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional ProbabilitiesTariq Ahmad, Allan Ramsay, Hanady Ahmed. 200-204 [doi]
- Yuan at SemEval-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural NetworkMin Wang, Xiaobing Zhou. 205-209 [doi]
- AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweetsMostafa Abdou, Artur Kulmizev, Joan Ginés i Ametllé. 210-217 [doi]
- Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment ClassificationAlon Rozental, Daniel Fleischer. 218-225 [doi]
- deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in TweetsZi Yuan Gao, Chia-Ping Chen. 226-230 [doi]
- ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning ModelsHuimin Xu, Man Lan, Yuanbin Wu. 231-235 [doi]
- EMA at SemEval-2018 Task 1: Emotion Mining for ArabicGilbert Badaro, Obeida El Jundi, Alaa Khaddaj, Alaa Maarouf, Raslan Kain, Hazem Hajj, Wassim El-Hajj. 236-244 [doi]
- NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer LearningChristos Baziotis, Athanasiou Nikolaos, Alexandra Chronopoulou, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Shrikanth Narayanan, Alexandros Potamianos. 245-255 [doi]
- CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective LexiconsRaj Kumar Gupta, Yinping Yang. 256-263 [doi]
- PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtagsJi-Ho Park, Peng Xu, Pascale Fung. 264-272 [doi]
- YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in TweetsYou Zhang, Jin Wang, Xuejie Zhang. 273-278 [doi]
- UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in SpanishMarloes Kuijper, Mike Lenthe, Rik Noord. 279-285 [doi]
- ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in TweetsMeng Li, Zhenyuan Dong, Zhihao Fan, Kongming Meng, Jinghua Cao, Guanqi Ding, Yuhan Liu, Jiawei Shan, Binyang Li. 286-290 [doi]
- TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention ArchitectureHardik Meisheri, Lipika Dey. 291-299 [doi]
- DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity predictionYoungmin Kim, Hyunju Lee. 300-304 [doi]
- DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation LearningHabibeh Naderi, Behrouz Haji Soleimani, Saif Mohammad, Svetlana Kiritchenko, Stan Matwin. 305-312 [doi]
- Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in TweetsZewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, Ran Wei. 313-318 [doi]
- Amrita_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in TweetsNidhin A. Unnithan, Shalini K, Barathi Ganesh H. B., M. Anand Kumar, Soman K. P. 319-323 [doi]
- SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature SelectionAngel Deborah S, Rajalakshmi S, S. Milton Rajendram, T. T. Mirnalinee. 324-328 [doi]
- CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in TweetsNaveen J. R, Barathi Ganesh H. B., M. Anand Kumar, Soman K. P. 329-333 [doi]
- TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion DetectionAnon George, Barathi Ganesh H. B., Anand Kumar M, Soman K. P. 334-338 [doi]
- IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity PredictionBhaskar Kotakonda, Prashanth Gowda, Brejesh Lall. 339-344 [doi]
- Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion DetectionPan Du, Jian-Yun Nie. 345-349 [doi]
- TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep LearningMalak Abdullah, Samira Shaikh. 350-357 [doi]
- RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep LearningVenkatesh Elango, Karan Uppal. 358-363 [doi]
- ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic TweetsEl Moatez Billah Nagoudi. 364-368 [doi]
- psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion AnalysisGrace Gee, Eugene Wang. 369-376 [doi]
- UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble ModelsAbhishek Avinash Narwekar, Roxana Girju. 377-384 [doi]
- KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in TweetsThomas Nyegaard-Signori, Casper Veistrup Helms, Johannes Bjerva, Isabelle Augenstein. 385-389 [doi]
- EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTMMan Liu. 390-394 [doi]
- UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword EmbeddingZhenduo Wang, Ted Pedersen. 395-399 [doi]
- UMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices?Jonathan Beaulieu, Dennis Asamoah Owusu. 400-404 [doi]
- The Dabblers at SemEval-2018 Task 2: Multilingual Emoji PredictionLarisa Alexa, Alina Beatrice Lorent, Daniela Gîfu, Diana Trandabat. 405-409 [doi]
- THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji PredictionChuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang. 410-414 [doi]
- #TeamINF at SemEval-2018 Task 2: Emoji Prediction in TweetsRita Almeida Ribeiro, João M. N. Silva. 415-418 [doi]
- EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji PredictionYufei Xie, Qingqing Song. 419-422 [doi]
- EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated WordsChen Shiyun, Maoquan Wang, He Liang. 423-427 [doi]
- Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji PredictionJing Chen, Dechuan Yang, Xilian Li, Wei Chen, Tengjiao Wang. 428-432 [doi]
- ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction TaskXingwu Lu, Xin Mao, Man Lan, Yuanbin Wu. 433-437 [doi]
- NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware AttentionChristos Baziotis, Athanasiou Nikolaos, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos. 438-444 [doi]
- Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji PredictionJoël Coster, Reinder Gerard Dalen, Nathalie Adriënne Jacqueline Stierman. 445-448 [doi]
- EPUTION at SemEval-2018 Task 2: Emoji Prediction with User AdaptionLiyuan Zhou, Qiongkai Xu, Hanna Suominen, Tom Gedeon. 449-453 [doi]
- PickleTeam! at SemEval-2018 Task 2: English and Spanish Emoji Prediction from TweetsDaphne Groot, Rémon Kruizinga, Hennie Veldthuis, Simon Wit, Hessel Haagsma. 454-458 [doi]
- YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji PredictionNan Wang, Jin Wang, Xuejie Zhang. 459-465 [doi]
- DUTH at SemEval-2018 Task 2: Emoji Prediction in TweetsDimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis. 466-469 [doi]
- TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji PredictionAngelo Basile, Kenny W. Lino. 470-476 [doi]
- SyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer PerceptronsFabio Massimo Zanzotto, Andrea Santilli. 477-481 [doi]
- Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and OversamplingShuning Jin, Ted Pedersen. 482-485 [doi]
- CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction RepresentationNaveen J. R, Hariharan V, Barathi Ganesh H. B., M. Anand Kumar, Soman K. P. 486-490 [doi]
- Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in TweetsLuciano Gerber, Matthew Shardlow. 491-496 [doi]
- Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector MachineDaniel Kopev, Atanas Atanasov, Dimitrina Zlatkova, Momchil Hardalov, Ivan Koychev, Ivelina Nikolova, Galia Angelova. 497-501 [doi]
- LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji PredictionGaël Guibon, Magalie Ochs, Patrice Bellot. 502-506 [doi]
- ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English TweetsKevin Swanberg, Madiha Mirza, Ted Pedersen, Zhenduo Wang. 507-511 [doi]
- NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment ModelMauro Dragoni. 512-519 [doi]
- UWB at SemEval-2018 Task 3: Irony detection in English tweetsTomás Hercig. 520-524 [doi]
- NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in TwitterThanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, Michael Trenell. 525-530 [doi]
- LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony DetectionBilal Ghanem, Francisco M. Rangel Pardo, Paolo Rosso. 531-536 [doi]
- IIIDYT at SemEval-2018 Task 3: Irony detection in English tweetsEdison Marrese-Taylor, Suzana Ilic, Jorge A. Balazs, Helmut Prendinger, Yutaka Matsuo. 537-540 [doi]
- PunFields at SemEval-2018 Task 3: Detecting Irony by Tools of Humor AnalysisElena Mikhalkova, Yuri Karyakin, Alexander Voronov, Dmitry Grigoriev, Artem Leoznov. 541-545 [doi]
- HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony DetectionWon-Ik Cho, Woo Hyun Kang, Nam Soo Kim. 546-552 [doi]
- WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of IronyOmid Rohanian, Shiva Taslimipoor, Richard Evans, Ruslan Mitkov. 553-559 [doi]
- Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony DetectionAidan San. 560-564 [doi]
- ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in TweetsJosé-Ángel González, Lluís F. Hurtado, Ferran Pla. 565-569 [doi]
- IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social mediaAniruddha Ghosh, Tony Veale. 570-575 [doi]
- CTSys at SemEval-2018 Task 3: Irony in TweetsMyan Sherif, Sherine Mamdouh, Wegdan Ghazi. 576-580 [doi]
- Irony Detector at SemEval-2018 Task 3: Irony Detection in English Tweets using Word GraphUsman Ahmed, Lubna Zafar, Faiza Qayyum, Muhammad Arshad Islam. 581-586 [doi]
- Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English TweetsEdward Dearden, Alistair Baron. 587-593 [doi]
- INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in TwitterDelia Irazú Hernández Farías, Fernando Sánchez-Vega, Manuel Montes-y-Gómez, Paolo Rosso. 594-599 [doi]
- ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning MethodsZhenghang Yin, Feixiang Wang, Man Lan, Wenting Wang. 600-606 [doi]
- KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony DetectionLuise Dürlich. 607-612 [doi]
- NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNsChristos Baziotis, Athanasiou Nikolaos, Pinelopi Papalampidi, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos. 613-621 [doi]
- YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on TwitterBo Peng, Jin Wang, Xuejie Zhang. 622-627 [doi]
- Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detectionNishant Nikhil, Muktabh Mayank Srivastava. 628-632 [doi]
- SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer PerceptronRajalakshmi S, Angel Deborah S, S. Milton Rajendram, T. T. Mirnalinee. 633-637 [doi]
- NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in TweetsHarsh Rangwani, Devang Kulshreshtha, Anil Kumar Singh. 638-642 [doi]
- ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English TweetsDelia Irazú Hernández Farías, Viviana Patti, Paolo Rosso. 643-648 [doi]
- #NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English TweetsEndang Wahyu Pamungkas, Viviana Patti. 649-654 [doi]
- KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling ProblemCheon-Eum Park, HeeJun Song, Changki Lee. 655-659 [doi]
- NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphsPiek Vossen. 660-666 [doi]
- FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering SystemCarla Abreu, Eugénio Oliveira. 667-673 [doi]
- NAI-SEA at SemEval-2018 Task 5: An Event Search SystemYingchi Liu, Quanzhi Li, Luo Si. 674-678 [doi]
- SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific PapersKata Gábor, Davide Buscaldi, Anne-Kathrin Schumann, Behrang QasemiZadeh, Haïfa Zargayouna, Thierry Charnois. 679-688 [doi]
- ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and ExtractionJonathan Rotsztejn, Nora Hollenstein, Ce Zhang. 689-696 [doi]
- SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)Peter Phandi, Amila Silva, Wei Lu. 697-706 [doi]
- DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic featuresChunping Ma, Huafei Zheng, Pengjun Xie, Chen Li, Linlin Li, Si Luo. 707-711 [doi]
- SemEval-2018 Task 9: Hypernym DiscoveryJosé Camacho-Collados, Claudio Delli Bovi, Luis Espinosa Anke, Sergio Oramas, Tommaso Pasini, Enrico Santus, Vered Shwartz, Roberto Navigli, Horacio Saggion. 712-724 [doi]
- CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym DiscoveryGabriel Bernier-Colborne, Caroline Barrière. 725-731 [doi]
- SemEval-2018 Task 10: Capturing Discriminative AttributesAlicia Krebs, Alessandro Lenci, Denis Paperno. 732-740 [doi]
- SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding FeaturesSunny Lai, Kwong-Sak Leung, Yee Leung. 741-746 [doi]
- SemEval-2018 Task 11: Machine Comprehension Using Commonsense KnowledgeSimon Ostermann 0002, Michael Roth, Ashutosh Modi, Stefan Thater, Manfred Pinkal. 747-757 [doi]
- Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine ComprehensionLiang Wang, Meng Sun, Wei Zhao, Kewei Shen, Jingming Liu. 758-762 [doi]
- SemEval-2018 Task 12: The Argument Reasoning Comprehension TaskIvan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein. 763-772 [doi]
- GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension taskHongseok Choi, Hyunju Lee. 773-777 [doi]
- LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation ClassificationTyler Renslow, Günter Neumann. 778-782 [doi]
- OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural NetworksDushyanta Dhyani. 783-787 [doi]
- The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept EmbeddingsYi Luan, Mari Ostendorf, Hannaneh Hajishirzi. 788-792 [doi]
- UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural NetworkVíctor Suárez-Paniagua, Isabel Segura-Bedmar, Akiko Aizawa. 793-797 [doi]
- MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural NetworkDi Jin, Franck Dernoncourt, Elena Sergeeva, Matthew McDermott, Geeticka Chauhan. 798-804 [doi]
- SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific PapersFarhad Nooralahzadeh, Lilja Øvrelid, Jan Tore Lønning. 805-810 [doi]
- IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation ClassificationZhongbo Yin, Zhunchen Luo, Wei Luo, Mao Bin, Tian Changhai, Yuming Ye, Shuai Wu. 811-815 [doi]
- Bf3R at SemEval-2018 Task 7: Evaluating Two Relation Extraction Tools for Finding Semantic Relations in Biomedical AbstractsMariana Neves, Daniel Butzke, Gilbert Schönfelder, Barbara Grune. 816-820 [doi]
- Texterra at SemEval-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific PapersAndrey Sysoev, Vladimir Mayorov. 821-825 [doi]
- UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific PublicationsThorsten Keiper, Zhonghao Lyu, Sara Pooladzadeh, Yuan Xu, Jingyi Zhang, Anne Lauscher, Simone Paolo Ponzetto. 826-830 [doi]
- GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation ClassificationSean MacAvaney, Luca Soldaini, Arman Cohan, Nazli Goharian. 831-835 [doi]
- ClaiRE at SemEval-2018 Task 7: Classification of Relations using EmbeddingsLena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho. 836-841 [doi]
- TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific TextsMartin Gluhak, Maria Pia di Buono, Abbas Akkasi, Jan Snajder. 842-847 [doi]
- NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network ModelMauro Dragoni. 848-852 [doi]
- SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and ClassificationDarshini Mahendran, Chathurika Brahmana, Bridget T. McInnes. 853-857 [doi]
- NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific PapersBiswanath Barik, Utpal Kumar Sikdar, Björn Gambäck. 858-862 [doi]
- Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural NetworksBhanu Pratap, Daniel Shank, Oladipo Ositelu, Byron Galbraith. 863-867 [doi]
- TeamDL at SemEval-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random FieldsManikandan. R, Krishna Madgula, Snehanshu Saha. 868-873 [doi]
- HCCL at SemEval-2018 Task 8: An End-to-End System for Sequence Labeling from Cybersecurity ReportsMingming Fu, Xuemin Zhao, Yonghong Yan 0002. 874-877 [doi]
- UMBC at SemEval-2018 Task 8: Understanding Text about MalwareAnkur Padia, Arpita Roy, Taneeya Satyapanich, Francis Ferraro, Shimei Pan, Youngja Park, Anupam Joshi, Tim Finin. 878-884 [doi]
- Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation LearningPablo Loyola, Kugamoorthy Gajananan, Yuji Watanabe, Fumiko Satoh. 885-889 [doi]
- Flytxt_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Naïve Bayes ClassifiersUtpal Kumar Sikdar, Biswanath Barik, Björn Gambäck. 890-893 [doi]
- Digital Operatives at SemEval-2018 Task 8: Using dependency features for malware NLPChris Brew. 894-897 [doi]
- Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic DependenciesMihaela Plamada-Onofrei, Ionut Hulub, Diana Trandabat, Daniela Gîfu. 898-902 [doi]
- SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term EmbeddingsZhuosheng Zhang, Jiangtong Li, Hai Zhao, Bingjie Tang. 903-908 [doi]
- NLP_HZ at SemEval-2018 Task 9: a Nearest Neighbor ApproachWei Qiu, Mosha Chen, Linlin Li, Luo Si. 909-913 [doi]
- UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word EmbeddingsArshia Zernab Hassan, Manikya Swathi Vallabhajosyula, Ted Pedersen. 914-918 [doi]
- EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym DiscoveryAhmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour. 919-923 [doi]
- ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised CorporaAlfredo Maldonado, Filip Klubicka. 924-927 [doi]
- 300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributesGábor Berend, Márton Makrai, Peter Földiák. 928-934 [doi]
- UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word DistributionsTomás Brychcín, Tomás Hercig, Josef Steinberger, Michal Konkol. 935-939 [doi]
- Meaning_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddingsPia Sommerauer, Antske Fokkens, Piek Vossen. 940-946 [doi]
- GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional SemanticsMohammed Attia, Younes Samih, Manaal Faruqui, Wolfgang Maier. 947-952 [doi]
- CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word AttributesPablo Gamallo 0001. 953-957 [doi]
- THU_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN modelChuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Yongfeng Huang. 958-962 [doi]
- ALB at SemEval-2018 Task 10: A System for Capturing Discriminative AttributesBogdan Dumitru, Alina Maria Ciobanu, Liviu P. Dinu. 963-967 [doi]
- ELiRF-UPV at SemEval-2018 Task 10: Capturing Discriminative Attributes with Knowledge Graphs and WikipediaJosé-Ángel González, Lluís F. Hurtado, Encarna Segarra, Ferran Pla. 968-971 [doi]
- Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and AssociationShiva Taslimipoor, Omid Rohanian, Le An Ha, Gloria Corpas Pastor, Ruslan Mitkov. 972-976 [doi]
- UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding ConesIgnacio Arroyo-Fernández, Ivan Vladimir Meza Ruiz, Carlos-Francisco Meéndez-Cruz. 977-984 [doi]
- Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational KnowledgeRobert Speer, Joanna Lowry-Duda. 985-989 [doi]
- BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative AttributesEnrico Santus, Chris Biemann, Emmanuele Chersoni. 990-994 [doi]
- Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative AttributesMaxim Grishin. 995-998 [doi]
- ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference DetectionYunxiao Zhou, Man Lan, Yuanbin Wu. 999-1002 [doi]
- AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representationVivek Vinayan, M. Anand Kumar, Soman K. P. 1003-1007 [doi]
- Discriminator at SemEval-2018 Task 10: Minimally Supervised DiscriminationArtur Kulmizev, Mostafa Abdou, Vinit Ravishankar, Malvina Nissim. 1008-1012 [doi]
- UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributesMilton King, Ali Hakimi Parizi, Paul Cook. 1013-1016 [doi]
- ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNetRui Mao, Guanyi Chen, Ruizhe Li, Chenghua Lin. 1017-1021 [doi]
- UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?Alexander Zhang, Marine Carpuat. 1022-1026 [doi]
- NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert KnowledgeYow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen. 1027-1033 [doi]
- ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense KnowledgeJosé-Ángel González, Lluís F. Hurtado, Encarna Segarra, Ferran Pla. 1034-1037 [doi]
- YNU_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensembleQingxun Liu, Yao Hongdou, Zhou Xaobing, Xie Ge. 1038-1042 [doi]
- YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine ComprehensionPeng Ding, Xiaobing Zhou. 1043-1047 [doi]
- ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension TaskYixuan Sheng, Man Lan, Yuanbin Wu. 1048-1052 [doi]
- CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual EntailmentZhengping Jiang, Qi Sun. 1053-1057 [doi]
- YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense KnowledgeHang Yuan, Jin Wang, Xuejie Zhang. 1058-1062 [doi]
- Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension TaskJiangnan Xia. 1063-1067 [doi]
- IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge SourceSofia Reznikova, Leon Derczynski. 1068-1072 [doi]
- Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning ModelsYongbin Li, Xiaobing Zhou. 1073-1077 [doi]
- MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense KnowledgeElizabeth M. Merkhofer, John C. Henderson, David Bloom, Laura Strickhart, Guido Zarrella. 1078-1082 [doi]
- SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning ComprehensionTaeuk Kim, Jihun Choi, Sang-goo Lee. 1083-1088 [doi]
- ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with AttentionWenjie Liu, Chengjie Sun, Lei Lin 0001, Bingquan Liu. 1089-1093 [doi]
- ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension TaskJunfeng Tian, Man Lan, Yuanbin Wu. 1094-1098 [doi]
- NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument ComprehensionTim Niven, Hung-Yu Kao. 1099-1103 [doi]
- BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical AttentionMeiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, Dong Yu. 1104-1108 [doi]
- YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention ModelQuanlei Liao, Xutao Yang, Jin Wang, Xuejie Zhang. 1109-1113 [doi]
- HHU at SemEval-2018 Task 12: Analyzing an Ensemble-based Deep Learning Approach for the Argument Mining Task of Choosing the Correct WarrantMatthias Liebeck, Andreas Funke, Stefan Conrad 0001. 1114-1119 [doi]
- YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning ComprehensionPeng Ding, Xiaobing Zhou. 1120-1123 [doi]
- UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym FuzzingAnirudh Joshi, Tim Baldwin, Richard O. Sinnott, Cécile Paris. 1124-1128 [doi]
- Joker at SemEval-2018 Task 12: The Argument Reasoning Comprehension with Neural AttentionSui Guobin, Wen-Han Chao, Zhunchen Luo. 1129-1132 [doi]
- TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought VectorsAna Brassard, Tin Kuculo, Filip Boltuzic, Jan Snajder. 1133-1136 [doi]
- Lyb3b at SemEval-2018 Task 12: Ensemble-based Deep Learning Models for Argument Reasoning Comprehension TaskYongbin Li, Xiaobing Zhou. 1137-1141 [doi]
- TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning ComprehensionZhimin Chen, Wei Song, Lizhen Liu. 1142-1145 [doi]