Dr Lea Frermann

  • Room: Level: 03 Room: 3.04
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • (Bayesian) Machine Learning for NLP
  • Computational Cognitive Modeling (language acquisition, categorization)
  • Natural Language Processing (question answering, summarization, common sense modeling, NLP on long texts, meaning change)

Biography

Lea Frermann is a Lecturer in the School of Computing and Information Systems, in the field of natural language processing (NLP). Her research interests focus on improving automatic understanding of long and complex texts (books, movie scripts), with help of access to common sense knowledge. She is also interested in using machine learning for a deeper understanding of human language processing, and in using such insights to improve automatic language understanding.

Prior to joining Melbourne University, Lea obtained a PhD from the University of Edinburgh. She was a postdoc at both University of Edinburgh and Amazon Research (Berlin), and spent research visits to a variety of institutions, including Stanford University and Columbia University (New York).

Recent publications

  1. Barrett, M.; Gonzalez-garduño, AV.; Frermann, L.; Søgaard, A. Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics. 2018, pp. 2017-2027. DOI: 10.18653/v1/n18-1184
  2. Frermann, L.; Cohen, SB.; Lapata, M. Whodunnit? Crime Drama as a Case for Natural Language Understanding. Transactions of the Association for Computational Linguistics. MIT Press - Journals. 2018, Vol. 6, pp. 1-15. DOI: 10.1162/tacl_a_00001
  3. Frermann, L. BAYESIAN MODELS OF CATEGORY ACQUISITION AND MEANING DEVELOPMENT. IEEE Intelligent Informatics Bulletin. IEEE. 2017, Vol. 18, Issue 1, pp. 23-23.
  4. Frermann, L.; Szarvas, G. Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2017, pp. 1873-1883. DOI: 10.18653/v1/d17-1200
  5. Frermann, L.; Lapata, M. A Bayesian Model of Diachronic Meaning Change. Transactions of the Association for Computational Linguistics. MIT Press - Journals. 2016, Vol. 4, pp. 31-45. DOI: 10.1162/tacl_a_00081
  6. Frermann, L.; Lapata, M. Incremental Bayesian Category Learning From Natural Language. Cognitive Science. WILEY. 2016, Vol. 40, Issue 6, pp. 1333-1381. DOI: 10.1111/cogs.12304
  7. Frermann, L.; Lapata, M. A Bayesian model for joint learning of categories and their features. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. The Association for Computational Linguistics. 2015, pp. 1576-1586. DOI: 10.3115/v1/N15-1181
  8. Frermann, L.; Titov, I.; Pinkal, M. A hierarchical Bayesian model for unsupervised induction of script knowledge. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. The Association for Computational Linguistics. 2014, pp. 49-57. DOI: 10.3115/v1/E14-1006
  9. Frermann, L.; Lapata, M. Incremental Bayesian learning of semantic categories. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. The Association for Computational Linguistics. 2014, pp. 249-258. DOI: 10.3115/v1/E14-1027
  10. Frermann, L.; Bond, F. Cross-lingual parse disambiguation based on semantic correspondence. 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference. The Association for Computational Linguistics. 2012, Vol. 2, pp. 125-129.