Dr Jey Han Lau

  • Room: Level: 03 Room: 25
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Natural Language Processing (Deep Learning, Language Models, Linguistic Knowledge)

Biography

Jey Han obtained his PhD in Computer Science from the University Melbourne in 2013, with a PhD thesis on LDA topic models. He then joined King's College London as a Research Associate, working on a project with to develop stochastic models that represent the syntactic knowledge that all native speakers share. Afterwards he went to IBM Research Australia as an industry scientist, spending over 3 years there before moving back to academia. Jey Han's general interest is in unsupervised learning, an area which develops algorithms to discover structure in languages with minimal or zero supervision. He has worked with applying these algorithms to variety of natural language problems, from discovering word meanings to detecting novel events in social media to predicting the well-formedness of a natural language sentence.

Recent publications

  1. Baldwin, T.; Cohn, T.; Brooke, J.; Lau, JH.; Hammond, A. Deep-speare: A joint neural model of poetic language, meter and rhyme. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). ACL Anthology. 2018, Vol. 1, pp. 1948-1958.
  2. Bernardy, JP.; Lappin, S.; Lau, JH. The influence of context on sentence acceptability judgements. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). 2018, Vol. 2, pp. 456-461.
  3. Zhang, WE.; Sheng, QZ.; Lau, JH.; Abebe, E. Detecting Duplicate Posts in Programming QA Communities via Latent Semantics and Association Rules. Proceedings of the 26th International Conference on World Wide Web - WWW '17. ASSOC COMPUTING MACHINERY. 2017, pp. 1221-1229. DOI: 10.1145/3038912.3052701
  4. Lau, JH.; Chi, L.; Tran, K-N.; Cohn, T. End-to-end Network for Twitter Geolocation Prediction and Hashing. CoRR. ACL Anthology. 2017, Vol. abs/1710.04802, pp. 744-753.
  5. Aletras, N.; Baldwin, T.; Lau, JH.; Stevenson, M. Evaluating topic representations for exploring document collections. Journal of the Association for Information Science and Technology. Association for Information Science and Technology (ASIS&T). 2017, Vol. 68, Issue 1, pp. 154-167. DOI: 10.1002/asi.23574
  6. Lau, JH.; Clark, A.; Lappin, S. Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge. Cognitive Science. WILEY. 2017, Vol. 41, Issue 5, pp. 1202-1241. DOI: 10.1111/cogs.12414
  7. Sorodoc, I.; Lau, JH.; Aletras, N.; Baldwin, T. Multimodal topic labelling. 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference. Unknown. 2017, Vol. 2, pp. 701-706.
  8. Lau, JH.; Baldwin, T.; Cohn, T. Topically Driven Neural Language Model. CoRR. ACL Anthology. 2017, Vol. 1, pp. 355-365. DOI: 10.18653/v1/P17-1033
  9. Lau, JH.; Baldwin, T. The sensitivity of topic coherence evaluation to topic cardinality. 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference. The Association for Computational Linguistics. 2016, pp. 483-487.
  10. Lau, JH.; Clark, A.; Lappin, S. Unsupervised prediction of acceptability judgements. ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference. 2015, Vol. 1, pp. 1618-1628.
  11. Lau, JH.; Cook, P.; Mccarthy, D.; Gella, S.; Baldwin, T. Learning word sense distributions, detecting unattested senses and identifying novel senses using topic models. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference. Association for Computing Machinery (ACM). 2014, Vol. 1, pp. 259-270.
  12. Lau, JH.; Newman, D.; Baldwin, TJ. Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. ACL Anthology. 2014, pp. 530-539.
  13. Cook, P.; Lau, JH.; Mccarthy, D.; Baldwin, T. Novel word-sense identification. COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers. Association for Computational Linguistics. 2014, pp. 1624-1635.
  14. Lau, JH.; Baldwin, T.; Newman, D. On collocations and topic models. ACM Transactions on Speech and Language Processing. Association for Computing Machinery (ACM). 2013, Vol. 10, Issue 3, pp. 1-14. DOI: 10.1145/2483969.2483972
  15. Lau, JH.; Cook, P.; Baldwin, T. Unimelb: Topic modelling-based word sense induction for web snippet clustering. *SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics. Association for Computational Linguistics (ACL). 2013, Vol. 2, pp. 218-221.
  16. Newman, D.; Lau, JH.; Grieser, K.; Baldwin, T. Automatic evaluation of topic coherence. Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media. The Association for Computational Linguistics. 2010, Vol. 1, Issue 1, pp. 100-108.
  17. Lau, JH.; Newman, D.; Karimi, S.; Baldwin, T. Best topic word selection for topic labelling. Proceedings of the 23rd International Conference on Computational Linguistics. The Association for Computational Linguistics. 2010, Vol. 2, Issue 1, pp. 605-613.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile