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ORCID12th SemEval@NAACL-HLT 2018: New Orleans, Louisiana, USA
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat:
Proceedings of The 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2018, New Orleans, Louisiana, USA, June 5-6, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-20-9 - Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko:
SemEval-2018 Task 1: Affect in Tweets. 1-17 - Venkatesh Duppada, Royal Jain, Sushant Hiray:
SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets. 18-23 - Francesco Barbieri, José Camacho-Collados, Francesco Ronzano, Luis Espinosa Anke, Miguel Ballesteros, Valerio Basile, Viviana Patti, Horacio Saggion:
SemEval 2018 Task 2: Multilingual Emoji Prediction. 24-33 - Çagri Çöltekin, Taraka Rama:
Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction. 34-38 - Cynthia Van Hee, Els Lefever, Véronique Hoste:
SemEval-2018 Task 3: Irony Detection in English Tweets. 39-50 - Chuhan Wu, Fangzhao Wu, Sixing Wu, Junxin Liu, Zhigang Yuan, Yongfeng Huang:
THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning. 51-56 - Jinho D. Choi, Henry Y. Chen:
SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. 57-64 - Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc, Matthijs Westera
, Gemma Boleda:
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library. 65-69 - Marten Postma, Filip Ilievski, Piek Vossen:
SemEval-2018 Task 5: Counting Events and Participants in the Long Tail. 70-80 - Paramita Mirza, Fariz Darari, Rahmad Mahendra:
KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents. 81-87 - Egoitz Laparra, Dongfang Xu, Ahmed Elsayed, Steven Bethard, Martha Palmer:
SemEval 2018 Task 6: Parsing Time Normalizations. 88-96 - Amy L. Olex, Luke Maffey, Nicholas Morgan, Bridget T. McInnes:
Chrono at SemEval-2018 Task 6: A System for Normalizing Temporal Expressions. 97-101 - Mauro Dragoni:
NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text. 102-108 - Maja Karasalo, Mattias Nilsson, Magnus Rosell, Ulrika Wickenberg Bolin:
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis. 109-115 - Zhengxin Zhang, Qimin Zhou, Hao Wu:
NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination. 116-122 - Luna De Bruyne, Orphée De Clercq, Véronique Hoste:
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets. 123-127 - Flor Miriam Plaza del Arco, Salud María Jiménez-Zafra, Maite Martín-Valdivia, Luis Alfonso Ureña López:
SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets. 128-132 - Pavel Pribán, Tomás Hercig, Ladislav Lenc:
UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets. 133-140 - Yanghoon Kim, Hwanhee Lee, Kyomin Jung:
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification. 141-145 - Mario Graff, Sabino Miranda-Jiménez, Eric Sadit Tellez, Daniela Moctezuma:
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis. 146-150 - Guillaume Daval-Frerot, Abdessalam Bouchekif, Anatole Moreau:
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach. 151-155 - Masaki Aono, Shinnosuke Himeno:
KDE-AFFECT at SemEval-2018 Task 1: Estimation of Affects in Tweet by Using Convolutional Neural Network for n-gram. 156-161 - Aysu Ezen-Can, Ethem F. Can:
RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem. 162-166 - Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babaoglu:
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification. 167-171 - Dmitry Kravchenko, Lidia Pivovarova:
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings. 172-176 - Ramona Andreea Turcu, Sandra Maria Amarandei, Iuliana Alexandra Flescan-Lovin-Arseni, Daniela Gîfu, Diana Trandabat:
EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #Tweets. 177-180 - Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, David Van Bruwaene:
uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network. 181-185 - Chuhan Wu, Fangzhao Wu, Junxin Liu, Zhigang Yuan, Sixing Wu, Yongfeng Huang:
THU_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM. 186-192 - Mohammed Jabreel, Antonio Moreno:
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets. 193-199 - Tariq Ahmad, Allan Ramsay, Hanady Ahmed:
CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities. 200-204 - Min Wang, Xiaobing Zhou:
Yuan at SemEval-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural Network. 205-209 - Mostafa Abdou, Artur Kulmizev, Joan Ginés i Ametllé:
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets. 210-217 - Alon Rozental, Daniel Fleischer:
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification. 218-225 - Zi Yuan Gao, Chia-Ping Chen:
deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets. 226-230 - Huimin Xu, Man Lan, Yuanbin Wu:
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models. 231-235 - Gilbert Badaro, Obeida El Jundi, Alaa Khaddaj, Alaa Maarouf, Raslan Kain, Hazem M. Hajj, Wassim El-Hajj:
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic. 236-244 - Christos Baziotis, Athanasiou Nikolaos, Alexandra Chronopoulou, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Shrikanth S. Narayanan, Alexandros Potamianos:
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning. 245-255 - Raj Kumar Gupta, Yinping Yang:
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons. 256-263 - Ji Ho Park, Peng Xu, Pascale Fung:
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags. 264-272 - You Zhang, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets. 273-278 - Marloes Kuijper, Mike van Lenthe, Rik van Noord:
UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish. 279-285 - Meng Li, Zhenyuan Dong, Zhihao Fan, Kongming Meng, Jinghua Cao, Guanqi Ding, Yuhan Liu, Jiawei Shan, Binyang Li:
ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets. 286-290 - Hardik Meisheri, Lipika Dey:
TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture. 291-299 - Youngmin Kim, Hyunju Lee:
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction. 300-304 - Habibeh Naderi, Behrouz Haji Soleimani, Saif M. Mohammad, Svetlana Kiritchenko, Stan Matwin:
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning. 305-312 - Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, Ran Wei:
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets. 313-318 - Nidhin A. Unnithan, Shalini K, Barathi Ganesh H. B., M. Anand Kumar, Soman K. P:
Amrita_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets. 319-323 - Angel Deborah S, Rajalakshmi Sivanaiah, Sakaya Milton Rajendram, T. T. Mirnalinee:
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection. 324-328 - Naveen J. R, Barathi Ganesh H. B., M. Anand Kumar, Soman K. P:
CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets. 329-333 - Anon George, Barathi Ganesh H. B., Anand Kumar M, Soman K. P:
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection. 334-338 - Bhaskar Kotakonda, Prashanth Gowda, Brejesh Lall:
IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction. 339-344 - Pan Du, Jian-Yun Nie:
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection. 345-349 - Malak Abdullah, Samira Shaikh:
TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning. 350-357 - Venkatesh Elango, Karan Uppal:
RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning. 358-363 - El Moatez Billah Nagoudi:
ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets. 364-368 - Grace Gee, Eugene Wang:
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis. 369-376 - Abhishek Avinash Narwekar, Roxana Girju:
UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models. 377-384 - Thomas Nyegaard-Signori, Casper Veistrup Helms, Johannes Bjerva, Isabelle Augenstein:
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets. 385-389 - Man Liu:
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM. 390-394 - Zhenduo Wang, Ted Pedersen:
UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding. 395-399 - Jonathan Beaulieu, Dennis Asamoah Owusu:
UMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices? 400-404 - Larisa Alexa, Alina Beatrice Lorent, Daniela Gîfu, Diana Trandabat:
The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction. 405-409 - Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang:
THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction. 410-414 - Rita Almeida Ribeiro, Nádia Félix F. da Silva:
#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets. 415-418 - Yufei Xie, Qingqing Song:
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction. 419-422 - Shiyun Chen, Maoquan Wang, Liang He:
EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words. 423-427 - Jing Chen, Dechuan Yang, Xilian Li, Wei Chen, Tengjiao Wang:
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction. 428-432 - Xingwu Lu, Xin Mao, Man Lan, Yuanbin Wu:
ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task. 433-437 - Christos Baziotis, Athanasiou Nikolaos, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos:
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention. 438-444 - Joël Coster, Reinder Gerard Dalen, Nathalie Adriënne Jacqueline Stierman:
Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji Prediction. 445-448 - Liyuan Zhou, Qiongkai Xu, Hanna Suominen, Tom Gedeon:
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption. 449-453 - Daphne Groot, Rémon Kruizinga, Hennie Veldthuis, Simon Wit, Hessel Haagsma:
PickleTeam! at SemEval-2018 Task 2: English and Spanish Emoji Prediction from Tweets. 454-458 - Nan Wang, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction. 459-465 - Dimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis:
DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets. 466-469 - Angelo Basile, Kenny W. Lino:
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction. 470-476 - Fabio Massimo Zanzotto, Andrea Santilli:
SyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons. 477-481 - Shuning Jin, Ted Pedersen:
Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling. 482-485 - Naveen J. R, Hariharan V, Barathi Ganesh H. B., M. Anand Kumar, Soman K. P:
CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation. 486-490 - Luciano Gerber, Matthew Shardlow:
Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets. 491-496 - Daniel Kopev, Atanas Atanasov, Dimitrina Zlatkova, Momchil Hardalov, Ivan Koychev, Ivelina Nikolova, Galia Angelova:
Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine. 497-501 - Gaël Guibon, Magalie Ochs, Patrice Bellot:
LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction. 502-506 - Kevin Swanberg, Madiha Mirza, Ted Pedersen, Zhenduo Wang:
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets. 507-511 - Mauro Dragoni:
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model. 512-519 - Tomás Hercig:
UWB at SemEval-2018 Task 3: Irony detection in English tweets. 520-524 - Thanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, Michael Trenell:
NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter. 525-530 - Bilal Ghanem, Francisco M. Rangel Pardo, Paolo Rosso:
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection. 531-536 - Edison Marrese-Taylor, Suzana Ilic, Jorge A. Balazs, Helmut Prendinger, Yutaka Matsuo:
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets. 537-540 - Elena Mikhalkova, Yuri Karyakin, Alexander Voronov, Dmitry Grigoriev, Artem Leoznov:
PunFields at SemEval-2018 Task 3: Detecting Irony by Tools of Humor Analysis. 541-545 - Won-Ik Cho, Woo Hyun Kang, Nam Soo Kim:
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection. 546-552 - Omid Rohanian, Shiva Taslimipoor, Richard Evans, Ruslan Mitkov:
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony. 553-559 - Aidan San:
Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection. 560-564 - José-Ángel González, Lluís-F. Hurtado, Ferran Pla:
ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets. 565-569 - Aniruddha Ghosh, Tony Veale:
IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media. 570-575 - Myan Sherif, Sherine Mamdouh, Wegdan Ghazi:
CTSys at SemEval-2018 Task 3: Irony in Tweets. 576-580 - Usman Ahmed, Lubna Zafar, Faiza Qayyum, Muhammad Arshad Islam:
Irony Detector at SemEval-2018 Task 3: Irony Detection in English Tweets using Word Graph. 581-586 - Edward Dearden, Alistair Baron:
Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets. 587-593 - Delia Irazú Hernández Farías, Fernando Sánchez-Vega, Manuel Montes-y-Gómez, Paolo Rosso:
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter. 594-599 - Zhenghang Yin, Feixiang Wang, Man Lan, Wenting Wang:
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods. 600-606 - Luise Dürlich:
KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection. 607-612 - Christos Baziotis, Athanasiou Nikolaos, Pinelopi Papalampidi, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos:
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs. 613-621 - Bo Peng, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter. 622-627 - Nishant Nikhil, Muktabh Mayank Srivastava:
Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection. 628-632 - Rajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee:
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron. 633-637 - Harsh Rangwani, Devang Kulshreshtha, Anil Kumar Singh:
NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets. 638-642 - Delia Irazú Hernández Farías, Viviana Patti, Paolo Rosso:
ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets. 643-648 - Endang Wahyu Pamungkas, Viviana Patti:
#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets. 649-654 - Cheon-Eum Park, Heejun Song, Changki Lee:
KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem. 655-659 - Piek Vossen:
NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs. 660-666 - Carla Abreu, Eugénio Oliveira:
FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System. 667-673 - Yingchi Liu, Quanzhi Li, Luo Si:
NAI-SEA at SemEval-2018 Task 5: An Event Search System. 674-678 - Kata Gábor, Davide Buscaldi, Anne-Kathrin Schumann, Behrang QasemiZadeh, Haïfa Zargayouna, Thierry Charnois:
SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers. 679-688 - Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang:
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction. 689-696 - Peter Phandi, Amila Silva, Wei Lu:
SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP). 697-706 - Chunping Ma, Huafei Zheng, Pengjun Xie, Chen Li, Linlin Li, Si Luo:
DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features. 707-711 - José Camacho-Collados, Claudio Delli Bovi, Luis Espinosa Anke, Sergio Oramas, Tommaso Pasini, Enrico Santus, Vered Shwartz, Roberto Navigli, Horacio Saggion:
SemEval-2018 Task 9: Hypernym Discovery. 712-724 - Gabriel Bernier-Colborne, Caroline Barrière:
CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery. 725-731 - Alicia Krebs, Alessandro Lenci, Denis Paperno:
SemEval-2018 Task 10: Capturing Discriminative Attributes. 732-740 - Sunny Lai, Kwong-Sak Leung, Yee Leung:
SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Features. 741-746 - Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, Manfred Pinkal:
SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge. 747-757 - Liang Wang, Meng Sun, Wei Zhao, Kewei Shen, Jingming Liu:
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension. 758-762 - Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein:
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task. 763-772 - Hongseok Choi, Hyunju Lee:
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task. 773-777 - Tyler Renslow, Günter Neumann:
LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification. 778-782 - Dushyanta Dhyani:
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks. 783-787 - Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi:
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings. 788-792 - Víctor Suárez-Paniagua, Isabel Segura-Bedmar, Akiko Aizawa:
UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network. 793-797 - Di Jin, Franck Dernoncourt, Elena Sergeeva, Matthew B. A. McDermott, Geeticka Chauhan:
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network. 798-804 - Farhad Nooralahzadeh, Lilja Øvrelid, Jan Tore Lønning:
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers. 805-810 - Zhongbo Yin, Zhunchen Luo, Wei Luo, Mao Bin, Changhai Tian, Yuming Ye, Shuai Wu:
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification. 811-815 - Mariana Neves, Daniel Butzke, Gilbert Schönfelder, Barbara Grune:
Bf3R at SemEval-2018 Task 7: Evaluating Two Relation Extraction Tools for Finding Semantic Relations in Biomedical Abstracts. 816-820 - Andrey Sysoev, Vladimir Mayorov:
Texterra at SemEval-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific Papers. 821-825 - Thorsten Keiper, Zhonghao Lyu, Sara Pooladzadeh, Yuan Xu, Jingyi Zhang, Anne Lauscher, Simone Paolo Ponzetto:
UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications. 826-830 - Sean MacAvaney, Luca Soldaini, Arman Cohan, Nazli Goharian:
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification. 831-835 - Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho:
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings. 836-841 - Martin Gluhak, Maria Pia di Buono, Abbas Akkasi, Jan Snajder:
TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts. 842-847 - Mauro Dragoni:
NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model. 848-852 - Darshini Mahendran, Chathurika Brahmana, Bridget T. McInnes:
SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification. 853-857 - Biswanath Barik, Utpal Kumar Sikdar, Björn Gambäck:
NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers. 858-862 - Bhanu Pratap, Daniel Shank, Oladipo Ositelu, Byron Galbraith:
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks. 863-867 - Manikandan R, Krishna Madgula, Snehanshu Saha:
TeamDL at SemEval-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields. 868-873 - Mingming Fu, Xuemin Zhao, Yonghong Yan:
HCCL at SemEval-2018 Task 8: An End-to-End System for Sequence Labeling from Cybersecurity Reports. 874-877 - Ankur Padia, Arpita Roy, Taneeya Satyapanich, Francis Ferraro, Shimei Pan, Youngja Park, Anupam Joshi, Tim Finin:
UMBC at SemEval-2018 Task 8: Understanding Text about Malware. 878-884 - Pablo Loyola, Kugamoorthy Gajananan, Yuji Watanabe, Fumiko Satoh:
Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning. 885-889 - Utpal Kumar Sikdar, Biswanath Barik, Björn Gambäck:
Flytxt_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Naïve Bayes Classifiers. 890-893 - Chris Brew:
Digital Operatives at SemEval-2018 Task 8: Using dependency features for malware NLP. 894-897 - Mihaela Plamada-Onofrei, Ionut Hulub, Diana Trandabat, Daniela Gîfu:
Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies. 898-902 - Zhuosheng Zhang, Jiangtong Li, Hai Zhao, Bingjie Tang:
SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings. 903-908 - Wei Qiu, Mosha Chen, Linlin Li, Luo Si:
NLP_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach. 909-913 - Arshia Zernab Hassan, Manikya Swathi Vallabhajosyula, Ted Pedersen:
UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings. 914-918 - Ahmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour:
EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery. 919-923 - Alfredo Maldonado, Filip Klubicka:
ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora. 924-927 - Gábor Berend, Márton Makrai, Peter Földiák:
300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes. 928-934 - Tomás Brychcín, Tomás Hercig, Josef Steinberger, Michal Konkol:
UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions. 935-939 - Pia Sommerauer, Antske Fokkens, Piek Vossen:
Meaning_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings. 940-946 - Mohammed Attia, Younes Samih, Manaal Faruqui, Wolfgang Maier:
GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics. 947-952 - Pablo Gamallo:
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes. 953-957 - Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Yongfeng Huang:
THU_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model. 958-962 - Bogdan Dumitru, Alina Maria Ciobanu, Liviu P. Dinu:
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes. 963-967 - José-Ángel González, Lluís-F. Hurtado, Encarna Segarra, Ferran Pla:
ELiRF-UPV at SemEval-2018 Task 10: Capturing Discriminative Attributes with Knowledge Graphs and Wikipedia. 968-971 - Shiva Taslimipoor, Omid Rohanian, Le An Ha, Gloria Corpas Pastor, Ruslan Mitkov:
Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and Association. 972-976 - Ignacio Arroyo-Fernández, Iván Vladimir Meza Ruíz, Carlos-Francisco Méndez-Cruz:
UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones. 977-984 - Robyn Speer, Joanna Lowry-Duda:
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge. 985-989 - Enrico Santus, Chris Biemann, Emmanuele Chersoni:
BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes. 990-994 - Maxim Grishin:
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes. 995-998 - Yunxiao Zhou, Man Lan, Yuanbin Wu:
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. 999-1002 - Vivek Vinayan, M. Anand Kumar, Soman K. P:
AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation. 1003-1007 - Artur Kulmizev, Mostafa Abdou, Vinit Ravishankar, Malvina Nissim:
Discriminator at SemEval-2018 Task 10: Minimally Supervised Discrimination. 1008-1012 - Milton King, Ali Hakimi Parizi, Paul Cook:
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes. 1013-1016 - Rui Mao, Guanyi Chen, Ruizhe Li, Chenghua Lin:
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet. 1017-1021 - Alexander Zhang, Marine Carpuat:
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes? 1022-1026 - Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen:
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge. 1027-1033 - José-Ángel González, Lluís-F. Hurtado, Encarna Segarra, Ferran Pla:
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. 1034-1037 - Qingxun Liu, Yao Hongdou, Zhou Xaobing, Xie Ge:
YNU_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble. 1038-1042 - Peng Ding, Xiaobing Zhou:
YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension. 1043-1047 - Yixuan Sheng, Man Lan, Yuanbin Wu:
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task. 1048-1052 - Zhengping Jiang, Qi Sun:
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment. 1053-1057 - Hang Yuan, Jin Wang, Xuejie Zhang:
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge. 1058-1062 - Jiangnan Xia:
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task. 1063-1067 - Sofia Reznikova, Leon Derczynski:
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source. 1068-1072 - Yongbin Li, Xiaobing Zhou:
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models. 1073-1077 - Elizabeth M. Merkhofer, John C. Henderson, David Bloom, Laura Strickhart, Guido Zarrella:
MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge. 1078-1082 - Taeuk Kim, Jihun Choi, Sang-goo Lee:
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension. 1083-1088 - Wenjie Liu, Chengjie Sun, Lei Lin, Bingquan Liu:
ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention. 1089-1093 - Junfeng Tian, Man Lan, Yuanbin Wu:
ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task. 1094-1098 - Tim Niven, Hung-Yu Kao:
NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension. 1099-1103 - Meiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, Dong Yu:
BLCU_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention. 1104-1108 - Quanlei Liao, Xutao Yang, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention Model. 1109-1113 - Matthias Liebeck, Andreas Funke, Stefan Conrad:
HHU at SemEval-2018 Task 12: Analyzing an Ensemble-based Deep Learning Approach for the Argument Mining Task of Choosing the Correct Warrant. 1114-1119 - Peng Ding, Xiaobing Zhou:
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension. 1120-1123 - Anirudh Joshi, Tim Baldwin, Richard O. Sinnott, Cécile Paris:
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing. 1124-1128 - Guobin Sui, Wen-Han Chao, Zhunchen Luo:
Joker at SemEval-2018 Task 12: The Argument Reasoning Comprehension with Neural Attention. 1129-1132 - Ana Brassard, Tin Kuculo, Filip Boltuzic, Jan Snajder:
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors. 1133-1136 - Yongbin Li, Xiaobing Zhou:
Lyb3b at SemEval-2018 Task 12: Ensemble-based Deep Learning Models for Argument Reasoning Comprehension Task. 1137-1141 - Zhimin Chen, Wei Song, Lizhen Liu:
TRANSRW at SemEval-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension. 1142-1145
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