Table of Contents

• Proceedings of the 13th International Workshop on Semantic EvaluationJonathan May, Ekaterina Shutova, Aurélie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad. [doi]
• SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCADaniel Hershcovich, Zohar Aizenbud, Leshem Choshen, Elior Sulem, Ari Rappoport, Omri Abend. 1-10 [doi]
• HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree ParsingWei Jiang, Zhenghua Li, Yu Zhang, Min Zhang. 11-15 [doi]
• SemEval-2019 Task 2: Unsupervised Lexical Frame InductionBehrang QasemiZadeh, Miriam R. L. Petruck, Regina Stodden, Laura Kallmeyer, Marie Candito. 16-30 [doi]
• Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame inductionNikolay Arefyev, Boris Sheludko, Adis Davletov, Dmitry Kharchev, Alex Nevidomsky, Alexander Panchenko. 31-38 [doi]
• SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in TextAnkush Chatterjee, Kedhar Nath Narahari, Meghana Joshi, Puneet Agrawal. 39-48 [doi]
• ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERTChenyang Huang, Amine Trabelsi, Osmar R. Zaïane. 49-53 [doi]
• SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in TwitterValerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, Manuela Sanguinetti. 54-63 [doi]
• Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet ClassificationJuan Manuel Pérez, Franco M. Luque. 64-69 [doi]
• FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in TwitterVijayasaradhi Indurthi, Bakhtiyar Syed, Manish Shrivastava 0001, Nikhil Chakravartula, Manish Gupta 0001, Vasudeva Varma. 70-74 [doi]
• SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, Ritesh Kumar. 75-86 [doi]
• NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional TransformersPing Liu, Wen Li, Liang Zou. 87-91 [doi]
• CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCAWeimin Lyu, Sheng Huang, Abdul Rafae Khan, Shengqiang Zhang, Weiwei Sun, Jia Xu. 92-96 [doi]
• DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA)Dang Tuan Nguyen, Trung Tran. 97-101 [doi]
• GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural NetworksShiva Taslimipoor, Omid Rohanian, Sara Moze. 102-106 [doi]
• MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence TaggingGabriel Marzinotto, Johannes Heinecke, Géraldine Damnati. 107-112 [doi]
• Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic ParsingTobias Pütz, Kevin Glocker. 113-118 [doi]
• UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based DecoderDian Yu, Kenji Sagae. 119-124 [doi]
• HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word EmbeddingsSaba Anwar, Dmitry Ustalov, Nikolay Arefyev, Simone Paolo Ponzetto, Chris Biemann, Alexander Panchenko. 125-129 [doi]
• L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word RepresentationsEugénio Ribeiro, Vânia Mendonça, Ricardo Ribeiro 0001, David Martins de Matos, Alberto Sardinha, Ana Lúcia Santos, Luísa Coheur. 130-136 [doi]
• BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion PredictionVachagan Gratian. 137-141 [doi]
• CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion ClassificationGenta Indra Winata, Andrea Madotto, Zhaojiang Lin, Jamin Shin, Yan Xu, Peng Xu, Pascale Fung. 142-147 [doi]
• CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual ConversationsYves Bestgen. 148-152 [doi]
• CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVMElham Mohammadi, Hessam Amini, Leila Kosseim. 153-158 [doi]
• CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short ConversationJoseph Cummings, Jason Wilson. 159-163 [doi]
• CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion DetectionChangjie Li, Yun Xing. 164-168 [doi]
• CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMsAna Valeria González-Garduño, Victor Petrén Bach Hansen, Joachim Bingel, Isabelle Augenstein, Anders Søgaard. 169-174 [doi]
• ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion DetectorRafal Poswiata. 175-179 [doi]
• CX-ST-RNM at SemEval-2019 Task 3: Fusion of Recurrent Neural Networks Based on Contextualized and Static Word Representations for Contextual Emotion DetectionMichal Perelkiewicz. 180-184 [doi]
• ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion AnalysisAkansha Jain, Ishita Aggarwal, Ankit Singh. 185-189 [doi]
• E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in TextHarsh Patel. 190-194 [doi]
• ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion DetectionJosé-Ángel González, Lluís F. Hurtado, Ferran Pla. 195-199 [doi]
• EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep LearningHani Al-Omari, Malak Abdullah, Nabeel Bassam. 200-204 [doi]
• EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in TextNikhil Chakravartula, Vijayasaradhi Indurthi. 205-209 [doi]
• EmoSense at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual ConversationsSergey Smetanin. 210-214 [doi]
• EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combinationAbdessalam Bouchekif, Praveen Joshi, Latifa Bouchekif, Haithem Afli. 215-219 [doi]
• Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion DetectionJoan Xiao. 220-224 [doi]
• GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approachBogdan Dumitru. 225-229 [doi]
• GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre CorpusShabnam Tafreshi, Mona T. Diab. 230-235 [doi]
• IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep LearningArik Pamnani, Rajat Goel, Jayesh Choudhari, Mayank Singh 0001. 236-240 [doi]
• KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterancesJasabanta Patro, Nitin Choudhary, Kalpit Chittora, Animesh Mukherjee 0001. 241-246 [doi]
• KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual ConversationNourah Alswaidan, Mohamed El-bachir Menai. 247-250 [doi]
• LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and ClassificationWaleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean. 251-255 [doi]
• MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment AnalysisYoonhyung Lee, Yanghoon Kim, Kyomin Jung. 256-260 [doi]
• MoonGrad at SemEval-2019 Task 3: Ensemble BiRNNs for Contextual Emotion Detection in DialoguesChandrakant Bothe, Stefan Wermter. 261-265 [doi]
• NELEC at SemEval-2019 Task 3: Think Twice Before Going DeepParag Agrawal, Anshuman Suri. 266-271 [doi]
• NL-FIIT at SemEval-2019 Task 3: Emotion Detection From Conversational Triplets Using Hierarchical EncodersMichal Farkas, Peter Lacko. 272-276 [doi]
• NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learningRolandos-Alexandros Potamias, Georgios Siolas. 277-281 [doi]
• ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNNPeixiang Zhong, Chunyan Miao. 282-286 [doi]
• PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention NetworkLuyao Ma, Long Zhang, Wei Ye 0004, Wenhui Hu. 287-291 [doi]
• Podlab at SemEval-2019 Task 3: The Importance of Being ShallowAndrew Nguyen, Tobin South, Nigel G. Bean, Jonathan Tuke, Lewis Mitchell. 292-296 [doi]
• SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep LearningZinedine Rebiai, Simon Andersen, Antoine Debrenne, Victor Lafargue. 297-301 [doi]
• Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in ConversationsJacob Anderson. 302-306 [doi]
• SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversationsFlor Miriam Plaza del Arco, M. Dolores Molina-González, Maite Martín, Luis Alfonso Ureña López. 307-311 [doi]
• SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational ClassificationSanghwan Bae, Jihun Choi, Sang-goo Lee. 312-317 [doi]
• SSN_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural NetworkB. Senthil Kumar, D. Thenmozhi, Chandrabose Aravindan, Srinethe Sharavanan. 318-323 [doi]
• SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networksMarco Polignano, Marco de Gemmis, Giovanni Semeraro. 324-329 [doi]
• SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot ConversationsAngelo Basile, Marc Franco-Salvador, Neha Pawar, Sanja Stajner, Mara Chinea-Rios, Yassine Benajiba. 330-334 [doi]
• TDBot at SemEval-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification ApproachSourabh Maity. 335-339 [doi]
• THU_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNNSuyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang. 340-344 [doi]
• THU-HCSI at SemEval-2019 Task 3: Hierarchical Ensemble Classification of Contextual Emotion in ConversationXihao Liang, Ye Ma, Mingxing Xu. 345-349 [doi]
• TokyoTech_NLP at SemEval-2019 Task 3: Emotion-related Symbols in Emotion DetectionZhishen Yang, Sam Vijlbrief, Naoaki Okazaki. 350-354 [doi]
• UAIC at SemEval-2019 Task 3: Extracting Much from LittleCristian Simionescu, Ingrid Stoleru, Diana Lucaci, Gheorghe Balan, Iulian Bute, Adrian Iftene. 355-359 [doi]
• YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual ConversationDawei Li, Jin Wang, Xuejie Zhang. 360-364 [doi]
• KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in TwitterUmme Aymun Siddiqua, Abu Nowshed Chy, Masaki Aono. 365-370 [doi]
• ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech DetectionArup Baruah, Ferdous A. Barbhuiya, Kuntal Dey. 371-376 [doi]
• Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual EmbeddingAlon Rozental, Dadi Biton. 377-381 [doi]
• CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in TwitterIqra Ameer, Muhammad Hammad Fahim Siddiqui, Grigori Sidorov, Alexander F. Gelbukh. 382-386 [doi]
• CiTIUS-COLE at SemEval-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual TweetsSattam Almatarneh, Pablo Gamallo 0002, Francisco J. Ribadas-Pena. 387-390 [doi]
• Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of HateMike Zhang, Roy David, Leon Graumans, Gerben Timmerman. 391-395 [doi]
• GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on TwitterDiego Benito, Oscar Araque, Carlos Angel Iglesias. 396-403 [doi]
• HATEMINER at SemEval-2019 Task 5: Hate speech detection against Immigrants and Women in Twitter using a Multinomial Naive Bayes ClassifierNikhil Chakravartula. 404-408 [doi]
• HATERecognizer at SemEval-2019 Task 5: Using Features and Neural Networks to Face Hate RecognitionVictor Nina-Alcocer. 409-415 [doi]
• GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approachGretel Liz De la Peña. 416-419 [doi]
• INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on TwitterAlison Ribeiro, Nádia Silva. 420-425 [doi]
• JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing MethodsYaakov HaCohen-Kerner, Elyashiv Shayovitz, Shalom Rochman, Eli Cahn, Gal Didi, Ziv Ben-David. 426-430 [doi]
• Know-Center at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter using CNNsKevin Winter, Roman Kern. 431-435 [doi]
• LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)Nina Bauwelinck, Gilles Jacobs, Véronique Hoste, Els Lefever. 436-440 [doi]
• ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in TwitterHuangpan Zhang, Michael Wojatzki, Tobias Horsmann, Torsten Zesch. 441-446 [doi]
• MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial FrameworkLuis Enrique Argota Vega, Jorge Carlos Reyes-Magaña, Helena Gómez-Adorno, Gemma Bel Enguix. 447-452 [doi]
• MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech DetectionAbigail Gertner, John C. Henderson, Elizabeth M. Merkhofer, Amy Marsh, Ben Wellner, Guido Zarrella. 453-459 [doi]
• Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech DetectionÒscar Garibo i Orts. 460-463 [doi]
• STUFIIT at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter with MUSE and ELMo EmbeddingsMichal Bojkovský, Matús Pikuliak. 464-468 [doi]
• Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text ClassificationMiriam Benballa, Sebastien Collet, Romain Picot-Clémente. 469-475 [doi]
• SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweetsFlor Miriam Plaza del Arco, M. Dolores Molina-González, Maite Martín, Luis Alfonso Ureña López. 476-479 [doi]
• SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasingArturo Montejo Ráez, Salud María Jiménez Zafra, Miguel A. García-Cumbreras, Manuel Carlos Díaz-Galiano. 480-483 [doi]
• sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech DetectionAria Nourbakhsh, Frida Vermeer, Gijs Wiltvank, Rob van der Goot. 484-488 [doi]
• The binary trio at SemEval-2019 Task 5: Multitarget Hate Speech Detection in TweetsPatricia Chiril, Farah Benamara Zitoune, Véronique Moriceau, Abhishek Kumar. 489-493 [doi]
• The Titans at SemEval-2019 Task 5: Detection of hate speech against immigrants and women in TwitterAvishek Garain, Arpan Basu. 494-497 [doi]
• TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and SpanishMihai Manolescu, Denise Löfflad, Adham Nasser Mohamed Saber, Masoumeh Moradipour Tari. 498-502 [doi]
• Tw-StAR at SemEval-2019 Task 5: N-gram embeddings for Hate Speech Detection in Multilingual TweetsHala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babaoglu. 503-507 [doi]
• UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech DetectionCarlos Perelló, David Tomás, Alberto Garcia-Garcia, José García Rodríguez, José Camacho-Collados. 508-513 [doi]
• UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive LanguageAli Hakimi Parizi, Milton King, Paul Cook. 514-518 [doi]
• UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural NetworksGustavo Henrique Paetzold, Marcos Zampieri, Shervin Malmasi. 519-523 [doi]
• Vista.ue at SemEval-2019 Task 5: Single Multilingual Hate Speech Detection ModelKashyap Raiyani, Teresa Gonçalves, Paulo Quaresma, Vítor Beires Nogueira. 524-528 [doi]
• YNU NLP at SemEval-2019 Task 5: Attention and Capsule Ensemble for Identifying Hate SpeechBin Wang, Haiyan Ding. 529-534 [doi]
• YNU_DYX at SemEval-2019 Task 5: A Stacked BiGRU Model Based on Capsule Network in Detection of HateYunxia Ding, Xiaobing Zhou, Xuejie Zhang. 535-539 [doi]
• Amrita School of Engineering - CSE at SemEval-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and ClassificationMurali Sridharan, Swapna T. R.. 540-546 [doi]
• bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social mediaRitesh Kumar, Bhanodai Guggilla, Rajendra Pamula, Maheshwar Reddy Chennuru. 547-550 [doi]
• BNU-HKBU UIC NLP Team 2 at SemEval-2019 Task 6: Detecting Offensive Language Using BERT modelZhenghao Wu, Hao Zheng, Jianming Wang, Weifeng Su, Jefferson Fong. 551-555 [doi]
• CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweetsGuy Aglionby, Christopher Davis, Pushkar Mishra, Andrew Caines, Helen Yannakoudakis, Marek Rei, Ekaterina Shutova, Paula Buttery. 556-563 [doi]
• CN-HIT-MI.T at SemEval-2019 Task 6: Offensive Language Identification Based on BiLSTM with Double AttentionYaojie Zhang, Bing Xu, Tiejun Zhao. 564-570 [doi]
• ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERTJohn Pavlopoulos, Nithum Thain, Lucas Dixon, Ion Androutsopoulos. 571-576 [doi]
• DA-LD-Hildesheim at SemEval-2019 Task 6: Tracking Offensive Content with Deep Learning using Shallow RepresentationSandip Modha, Prasenjit Majumder, Daksh Patel. 577-581 [doi]
• DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweetsGretel Liz De la Peña Sarracén, Paolo Rosso. 582-586 [doi]
• NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural NetworksPrashant Kapil, Asif Ekbal, Dipankar Das 0001. 587-592 [doi]
• Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive TweetsTed Pedersen. 593-599 [doi]
• Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approachesEmad Kebriaei, Samaneh Karimi, Nazanin Sabri, Azadeh Shakery. 600-603 [doi]
• Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning ApproachesAndraz Pelicon, Matej Martinc, Petra Kralj Novak. 604-610 [doi]
• Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence EmbeddingsVijayasaradhi Indurthi, Bakhtiyar Syed, Manish Shrivastava 0001, Manish Gupta 0001, Vasudeva Varma. 611-616 [doi]
• Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language IdentificationEhsan Doostmohammadi, Hossein Sameti, Ali Saffar. 617-621 [doi]
• HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and CategorizationHimanshu Bansal, Daniel Nagel, Anita Soloveva. 622-627 [doi]
• HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMoAlexander Oberstrass, Julia Romberg, Anke Stoll, Stefan Conrad 0001. 628-634 [doi]
• Hope at SemEval-2019 Task 6: Mining social media language to discover offensive languageGabriel Florentin Patras, Diana Florina Lungu, Daniela Gîfu, Diana Trandabat. 635-638 [doi]
• INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text ClassificationMario Graff, Sabino Miranda-Jiménez, Eric Sadit Tellez, Daniela Alejandra Ochoa. 639-644 [doi]
• JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing MethodsYaakov HaCohen-Kerner, Ziv Ben-David, Gal Didi, Eli Cahn, Shalom Rochman, Elyashiv Shayovitz. 645-651 [doi]
• jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social MediaJiahui Han, Shengtan Wu, Xinyu Liu. 652-656 [doi]
• JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural NetworksJohnny Torres, Carmen Vaca. 657-661 [doi]
• JU_ETCE_17_21 at SemEval-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in TweetsPreeti Mukherjee, Mainak Pal, Somnath Banerjee, Sudip Kumar Naskar. 662-667 [doi]
• KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detectionPriya Rani, Atul kr. Ojha. 668-671 [doi]
• LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM modelLutfiye Seda Mut Altin, Àlex Bravo Serrano, Horacio Saggion. 672-677 [doi]
• LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing OffensivenessPiush Aggarwal, Tobias Horsmann, Michael Wojatzki, Torsten Zesch. 678-682 [doi]
• MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from TwitterDebanjan Mahata, Haimin Zhang, Karan Uppal, Yaman Kumar, Rajiv Ratn Shah, Simra Shahid, Laiba Mehnaz, Sarthak Anand. 683-690 [doi]
• Nikolov-Radivchev at SemEval-2019 Task 6: Offensive Tweet Classification with BERT and EnsemblesAlex Nikolov, Victor Radivchev. 691-695 [doi]
• NIT_Agartala_NLP_Team at SemEval-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in Twitter Social Media CorporaSteve Durairaj Swamy, Anupam Jamatia, Björn Gambäck, Amitava Das. 696-703 [doi]
• NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNsJonathan Rusert, Padmini Srinivasan. 704-711 [doi]
• NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifierAlessandro Seganti, Helena Sobol, Iryna Orlova, Hannam Kim, Jakub Staniszewski, Tymoteusz Krumholc, Krystian Koziel. 712-721 [doi]
• nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language DetectionJelena Mitrovic, Bastian Birkeneder, Michael Granitzer. 722-726 [doi]
• Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep LearningPardeep Singh, Satish Chand. 727-734 [doi]
• SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social mediaFlor Miriam Plaza del Arco, M. Dolores Molina-González, Maite Martín, Luis Alfonso Ureña López. 735-738 [doi]
• SSN_NLP at SemEval-2019 Task 6: Offensive Language Identification in Social Media using Traditional and Deep Machine Learning ApproachesD. Thenmozhi, B. Senthil Kumar, Srinethe Sharavanan, Chandrabose Aravindan. 739-744 [doi]
• Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?Paula Fortuna, Juan Soler Company, Sérgio Nunes. 745-752 [doi]
• TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural NetworksAngel Suseelan, Rajalakshmi S, Logesh B, Harshini S, Geetika B, Dyaneswaran S, S. Milton Rajendram, T. T. Mirnalinee. 753-758 [doi]
• The Titans at SemEval-2019 Task 6: Offensive Language Identification, Categorization and Target IdentificationAvishek Garain, Arpan Basu. 759-762 [doi]
• TüKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text ClassificationMadeeswaran Kannan, Lukas Stein. 763-769 [doi]
• TUVD team at SemEval-2019 Task 6: Offense Target IdentificationElena Shushkevich, John Cardiff, Paolo Rosso. 770-774 [doi]
• UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training DataArun Rajendran, Chiyu Zhang, Muhammad Abdul-Mageed. 775-781 [doi]
• UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language DetectionGregor Wiedemann, Eugen Ruppert, Chris Biemann. 782-787 [doi]
• UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMsJian Zhu, Zuoyu Tian, Sandra Kübler. 788-795 [doi]
• USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word EmbeddingsBharti Goel, Ravi Sharma. 796-800 [doi]
• UTFPR at SemEval-2019 Task 6: Relying on Compositionality to Find OffenseGustavo Henrique Paetzold. 801-805 [doi]
• UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine LearningMurugesan Ramakrishnan, Wlodek Zadrozny, Narges Tabari. 806-811 [doi]
• YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on TwitterChengjin Zhou, Jin Wang, Xuejie Zhang. 812-817 [doi]
• YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive languageBin Wang, Xiaobing Zhou, Xuejie Zhang. 818-822 [doi]
• Zeyad at SemEval-2019 Task 6: That's Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in TweetsZeyad El-Zanaty. 823-828 [doi]
• SemEval-2019 Task 4: Hyperpartisan News DetectionJohannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, Martin Potthast. 829-839 [doi]
• Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional NetworkYe Jiang, Johann Petrak, Xingyi Song, Kalina Bontcheva, Diana Maynard. 840-844 [doi]
• SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for RumoursGenevieve Gorrell, Ahmet Aker, Kalina Bontcheva, Leon Derczynski, Elena Kochkina, Maria Liakata, Arkaitz Zubiaga. 845-854 [doi]
• eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation InformationQuanzhi Li, Qiong Zhang, Luo Si. 855-859 [doi]
• SemEval-2019 Task 8: Fact Checking in Community Question Answering ForumsTsvetomila Mihaylova, Georgi Karadzhov, Pepa Atanasova, Ramy Baly, Mitra Mohtarami, Preslav Nakov. 860-869 [doi]
• AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community ForumsZhengwei Lv, Duoxing Liu, Haifeng Sun, Xiao Liang, Tao Lei, Zhizhong Shi, Feng Zhu, Lei Yang. 870-876 [doi]
• SemEval-2019 Task 9: Suggestion Mining from Online Reviews and ForumsSapna Negi, Tobias Daudert, Paul Buitelaar. 877-887 [doi]
• m_y at SemEval-2019 Task 9: Exploring BERT for Suggestion MiningMasahiro Yamamoto, Toshiyuki Sekiya. 888-892 [doi]
• SemEval-2019 Task 10: Math Question AnsweringMark Hopkins, Ronan Le Bras, Cristian Petrescu-Prahova, Gabriel Stanovsky, Hannaneh Hajishirzi, Rik Koncel-Kedziorski. 893-899 [doi]
• AiFu at SemEval-2019 Task 10: A Symbolic and Sub-symbolic Integrated System for SAT Math Question AnsweringYifan Liu, Keyu Ding, Yi Zhou. 900-906 [doi]
• SemEval-2019 Task 12: Toponym Resolution in Scientific PapersDavy Weissenbacher, Arjun Magge, Karen O'Connor, Matthew Scotch, Graciela Gonzalez-Hernandez. 907-916 [doi]
• DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym ResolutionXiaoBin Wang, Chunping Ma, Huafei Zheng, Chu Liu, Pengjun Xie, Linlin Li, Luo Si. 917-923 [doi]
• Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News DetectionOlga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris. 924-928 [doi]
• Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News DetectionCarla Pérez-Almendros, Luis Espinosa Anke, Steven Schockaert. 929-933 [doi]
• Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News DetectionViresh Gupta, Baani Leen Kaur Jolly, Ramneek Kaur, Tanmoy Chakraborty 0002. 934-938 [doi]
• Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News DetectionTim Isbister, Fredrik Johansson. 939-943 [doi]
• Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised FeaturesRodrigo Agerri. 944-948 [doi]
• Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News DetectorSaptarshi Sengupta, Ted Pedersen. 949-953 [doi]
• Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News DetectorNikhil Chakravartula, Vijayasaradhi Indurthi, Bakhtiyar Syed. 954-956 [doi]
• Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News DetectorCelena Chen, Celine Park, Jason Dwyer, Julie Medero. 957-961 [doi]
• Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News DetectorMehdi Drissi, Pedro Sandoval Segura, Vivaswat Ojha, Julie Medero. 962-966 [doi]
• Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News DetectorEvan Amason, Jake Palanker, Mary Clare Shen, Julie Medero. 967-970 [doi]
• NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News DetectorDuc-Vu Nguyen, Thin Dang, Ngan Nguyen. 971-975 [doi]
• Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based ClassifierJürgen Knauth. 976-980 [doi]
• Rouletabille at SemEval-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan PublishersJose G. Moreno, Yoann Pitarch, Karen Pinel-Sauvagnat, Gilles Hubert. 981-984 [doi]
• Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News DetectionAmal Alabdulkarim, Tariq Alhindi. 985-989 [doi]
• Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan NewsYoungjun Joo, Inchon Hwang. 990-994 [doi]
• TakeLab at SemEval-2019 Task 4: Hyperpartisan News DetectionNiko Palic, Juraj Vladika, Dominik Cubelic, Ivan Lovrencic, Maja Buljan, Jan Snajder. 995-998 [doi]
• Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News DetectionAndre Ferreira Cruz, Gil Rocha, Rui Sousa-Silva, Henrique Lopes Cardoso. 999-1003 [doi]
• Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined MetatopicsNazanin Afsarmanesh, Jussi Karlgren, Peter Sumbler, Nina Viereckel. 1004-1006 [doi]
• Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERTOsman Mutlu, Ozan Arkan Can, Erenay Dayanik. 1007-1011 [doi]
• Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan NewsDaniel Shaprin, Giovanni Da San Martino, Alberto Barrón-Cedeño, Preslav Nakov. 1012-1015 [doi]
• Team Kermit-the-frog at SemEval-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic FeaturesTalita Anthonio, Lennart Kloppenburg. 1016-1020 [doi]
• Team Kit Kittredge at SemEval-2019 Task 4: LSTM Voting SystemRebekah Cramerus, Tatjana Scheffler. 1021-1025 [doi]
• Team Ned Leeds at SemEval-2019 Task 4: Exploring Language Indicators of Hyperpartisan ReportingBozhidar Stevanoski, Sonja Gievska. 1026-1031 [doi]
• Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural NetworksMichael Färber 0001, Agon Qurdina, Lule Ahmedi. 1032-1036 [doi]
• Team Peter-Parker at SemEval-2019 Task 4: BERT-Based Method in Hyperpartisan News DetectionZhiyuan Ning, Yuanzhen Lin, Ruichao Zhong. 1037-1040 [doi]
• Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News DetectionAbdelrhman Saleh, Ramy Baly, Alberto Barrón-Cedeño, Giovanni Da San Martino, Mitra Mohtarami, Preslav Nakov, James Glass. 1041-1046 [doi]
• Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural NetworksAlbin Zehe, Lena Hettinger, Stefan Ernst, Christian Hauptmann, Andreas Hotho. 1047-1051 [doi]
• Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled DataNayeon Lee, Zihan Liu, Pascale Fung. 1052-1056 [doi]
• The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4Kazuaki Hanawa, Shota Sasaki, Hiroki Ouchi, Jun Suzuki, Kentaro Inui. 1057-1061 [doi]
• Tintin at SemEval-2019 Task 4: Detecting Hyperpartisan News Article with only Simple TokensYves Bestgen. 1062-1066 [doi]
• Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVMChia-Lun Yeh, Babak Loni, Anne Schuth. 1067-1071 [doi]
• UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMsChiyu Zhang, Arun Rajendran, Muhammad Abdul-Mageed. 1072-1077 [doi]
• Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic FeaturesVertika Srivastava, Ankita Gupta, Divya Prakash, Sudeep Kumar Sahoo, Rohit R. R, Yeon Hyang Kim. 1078-1082 [doi]
• AndrejJan at SemEval-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour AnalysisAndrej Janchevski, Sonja Gievska. 1083-1089 [doi]
• BLCU_NLP at SemEval-2019 Task 7: An Inference Chain-based GPT Model for Rumour EvaluationRuoyao Yang, Wanying Xie, Chunhua Liu, Dong Yu. 1090-1096 [doi]
• BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional TransformersMartin Fajcik, Pavel Smrz, Lukás Burget. 1097-1104 [doi]
• CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against RumorsIpek Baris, Lukas Schmelzeisen, Steffen Staab. 1105-1109 [doi]
• Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour VerificationZhuoran Liu, Shivali Goel, Mukund Yelahanka Raghuprasad, Smaranda Muresan. 1110-1114 [doi]
• GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social MediaSardar Hamidian, Mona T. Diab. 1115-1119 [doi]
• SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal ExpressionsMiguel A. García-Cumbreras, Salud María Jiménez Zafra, Arturo Montejo Ráez, Manuel Carlos Díaz-Galiano, Estela Saquete. 1120-1124 [doi]
• UPV-28-UNITO at SemEval-2019 Task 7: Exploiting Post's Nesting and Syntax Information for Rumor Stance ClassificationBilal Ghanem, Alessandra Teresa Cignarella, Cristina Bosco, Paolo Rosso, Francisco Manuel Rangel Pardo. 1125-1131 [doi]
• BLCU_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact CheckingWanying Xie, Mengxi Que, Ruoyao Yang, Chunhua Liu, Dong Yu. 1132-1137 [doi]
• CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question AnsweringAdithya Avvaru, Anupam Pandey. 1138-1143 [doi]
• ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuningTuhin Chakrabarty, Smaranda Muresan. 1144-1148 [doi]
• DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering ForumsDominik Stammbach, Stalin Varanasi, Guenter Neumann. 1149-1154 [doi]
• DUTH at SemEval-2019 Task 8: Part-Of-Speech Features for Question ClassificationAnastasios Bairaktaris, Symeon Symeonidis, Avi Arampatzis. 1155-1159 [doi]
• Fermi at SemEval-2019 Task 8: An elementary but effective approach to Question Discernment in Community QA ForumsBakhtiyar Syed, Vijayasaradhi Indurthi, Manish Shrivastava 0001, Manish Gupta 0001, Vasudeva Varma. 1160-1164 [doi]
• SolomonLab at SemEval-2019 Task 8: Question Factuality and Answer Veracity Prediction in Community ForumsAnkita Gupta, Sudeep Kumar Sahoo, Divya Prakash, Rohit R. R, Vertika Srivastava, Yeon Hyang Kim. 1165-1171 [doi]
• TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering ForumsPiotr Niewinski, Aleksander Wawer, Maria Pszona, Maria Janicka. 1172-1175 [doi]
• TueFact at SemEval 2019 Task 8: Fact checking in community question answering forums: context mattersRéka Juhász, Franziska Barbara Linnenschmidt, Teslin Roys. 1176-1179 [doi]
• YNU-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community ForumsPeng Liu, Jin Wang, Xuejie Zhang. 1180-1184 [doi]
• DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-SuggestionTirana Fatyanosa, Al Hafiz Akbar Maulana Siagian, Masayoshi Aritsugi. 1185-1191 [doi]
• DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classificationTobias Cabanski. 1192-1198 [doi]
• Hybrid RNN at SemEval-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion MiningAysu Ezen-Can, Ethem F. Can. 1199-1203 [doi]
• INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted FeaturesIlia Markov, Éric Villemonte de la Clergerie. 1204-1207 [doi]
• Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forumsJunyi Li. 1208-1212 [doi]
• MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFitSarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra Shahid, Haimin Zhang, Yaman Kumar, Rajiv Ratn Shah, Karan Uppal. 1213-1217 [doi]
• NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion MiningSamuel Pecar, Marián Simko, Mária Bieliková. 1218-1223 [doi]
• NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wildRolandos-Alexandros Potamias, Alexandros Neofytou, Georgios Siolas. 1224-1230 [doi]
• OleNet at SemEval-2019 Task 9: BERT based Multi-Perspective Models for Suggestion MiningJiaxiang Liu, Shuohuan Wang, Yu Sun. 1231-1236 [doi]
• SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented DataRajalakshmi S, Angel Suseelan, S. Milton Rajendram, T. T. Mirnalinee. 1237-1241 [doi]
• Suggestion Miner at SemEval-2019 Task 9: Suggestion Detection in Online Forum using Word GraphUsman Ahmed, Humera Liaquat, Luqman Ahmed, Syed Jawad Hussain. 1242-1246 [doi]
• Team Taurus at SemEval-2019 Task 9: Expert-informed pattern recognition for suggestion miningNelleke Oostdijk, Hans van Halteren. 1247-1253 [doi]
• ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samplesCheon-Eum Park, Juae Kim, Hyeon-gu Lee, Reinald Kim Amplayo, Harksoo Kim, Jungyun Seo, Changki Lee. 1254-1261 [doi]
• WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion MiningMateusz Klimaszewski, Piotr Andruszkiewicz. 1262-1266 [doi]
• Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented ApproachesYimeng Zhuang. 1267-1271 [doi]
• YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining ClassificationYunxia Ding, Xiaobing Zhou, Xuejie Zhang. 1272-1276 [doi]
• YNU-HPCC at SemEval-2019 Task 9: Using a BERT and CNN-BiLSTM-GRU Model for Suggestion MiningPing Yue, Jin Wang, Xuejie Zhang. 1277-1281 [doi]
• Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion MiningSai Prasanna, Sri Ananda Seelan. 1282-1286 [doi]
• ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion MiningQimin Zhou, Zhengxin Zhang, Hao Wu 0010, Linmao Wang. 1287-1291 [doi]
• ProblemSolver at SemEval-2019 Task 10: Sequence-to-Sequence Learning and Expression TreesXuefeng Luo, Alina Baranova, Jonas Biegert. 1292-1296 [doi]
• RGCL-WLV at SemEval-2019 Task 12: Toponym DetectionAlistair Plum, Tharindu Ranasinghe, Pablo Calleja, Constantin Orasan, Ruslan Mitkov. 1297-1301 [doi]
• THU_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific PapersTao Qi, Suyu Ge, Chuhan Wu, Yubo Chen 0002, Yongfeng Huang. 1302-1307 [doi]
• UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific PapersMatthew Magnusson, Laura Dietz. 1308-1312 [doi]
• UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolutionHaonan Li 0003, Minghan Wang, Timothy Baldwin, Martin Tomko, Maria Vasardani. 1313-1318 [doi]
• University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation EntitiesVikas Yadav, Egoitz Laparra, Ti-Tai Wang, Mihai Surdeanu, Steven Bethard. 1319-1323 [doi]