Janyce M. Wiebe

Professor

Email:
Office: 5409 Sennott Square
Mailbox: 297
Telephone:412-624-9590
Website: http://www.cs.pitt.edu/~wiebe

Dr. Wiebe's research areas are artificial intelligence and natural language processing (NLP). Her work with students and colleagues has been in discourse processing, word-sense disambiguation, pragmatics, and probabilistic classification in NLP. Her most recent work investigates automatically recognizing opinionated and evaluative language to support NLP applications such as question answering, information extraction, text categorization, and summarization.

Other projects include temporal reference resolution, event categorization, and an approach to analyzing agreement among human judges in terms of models of interaction, for the purpose of refining the set of annotation classes.

Five Most Recent Publications

L. Deng and J. Wiebe, "MPQA 3.0: An Entity/Event-Level Sentiment Corpus," NAACL 2015, pp. 1323-1328, Denver, Colorado, June 2015..

G. Trivedi, P. Pham, W. Chapman, R. Hwa, J. Wiebe and H. Hochheiser, "An Interactive Tool for Natural Language Processing on Clinical Text ," International Conference on Intelligent User Interfaces (IUI 2015), Atlanta, Georgia, 2015.

L. Deng, J. Wiebe, and Y. Choi, "Joint Inference and Disambiguation of Implicit Sentiments via Implicature Constraints," International Conference on Computational Linguistics (COLING 2014), pp. 79-88, Dublin City University, 2014.

L. Deng and J. Wiebe, "Sentiment Propagation via Implicature Constraints," Meeting of the European Chapter of the Association for Computational Linguistics (EACL 2014), Gothenburg, Sweden, 2014.

L. Deng and J. Wiebe, "Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models," Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbon, Portugal, 2015.

Research Interests

  • Artificial intelligence
  • Natural language processing/computational linguistics
  • Discourse processing
  • Lexical semantics
  • Subjectivity and sentiment analysis