Character Mining

Character Mining

This project presents a formidable task for machine comprehension in the realm of multiparty dialogue. The project's primary goal is to discern both explicit and implicit contexts surrounding individual characters by analyzing their conversations. As an open-source initiative, it offers valuable resources for tackling the following tasks:

Director

  • Jinho Choi - Assistant Professor at Emory University

Related Project

Distribution

Publications

  1. Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering. Li, C.; and Choi, J. D. Proceedings of the Annual Conference of the Association for Computational Linguistics (ACL), 2020.
  2. Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings. Jiang, H.; Zhang, X.; and Choi, J. D. Proceedings of the AAAI Conference on Artificial Intelligence: Student Abstract and Poster Program (AAAI:SAP), 2020.
  3. Design and Challenges of Cloze-Style Reading Comprehension Tasks on Multiparty Dialogue. Li, C.; Liu, T.; and Choi, J. D. arXiv, 1911.00773, 2019.
  4. FriendsQA: Open-Domain Question Answering on TV Show Transcripts. Yang, Z.; and Choi, J. D. Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), 2019.
  5. They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking. Zhou, E.; and Choi, J. D. Proceedings of the International Conference on Computational Linguistics (COLING), 2018.
  6. SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. Choi, J. D.; and Chen, H. Y. Proceedings of the International Workshop on Semantic Evaluation (SemEval'18) 2018.
  7. Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog. Ma, K.; Jurczyk, T.; and Choi, J. D. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
  8. Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks. Zahiri, S.; and Choi, J. D. Proceedings of the AAAI Workshop on Affective Content Analysis (AFFCON), 2018.
  9. Cross-domain Document Retrieval: Matching between Conversational and Formal Writings. Jurczyk, T.; and Choi, J. D. Proceedings of the EMNLP Workshop on Building Linguistically Generalizable NLP Systems (BLGNLP), 2017.
  10. Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks. Ma, K.; Xiao, C.; and Choi, J. D. Proceedings of the Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL:SRW), 2017.
  11. Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts. Chen, H. Y.; Zhou, E.; and Choi, J. D. Proceedings of the Conference on Computational Natural Language Learning (CoNLL), 2017.
  12. Character Identification on Multiparty Conversation: Identifying Mentions of Characters in TV Shows. Chen, H. Y.; and Choi, J. D. Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), 2016.