Professor Tim Baldwin

  • Room: Level: 03 Room: 311
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Natural Language Processing (Text mining, social media analytics, text anallytics)

Personal webpage


Prof Timothy Baldwin is a Professor in the School of Computing and Information Systems, The University of Melbourne, Director of the ARC Training Centre in Cognitive Computing for Medical Technologies, and Associate Dean Research Training in the Melbourne School of Engineering. He was an ARC Future Fellow from 2013 to 2016, and has previously held visiting positions at Cambridge University, the University of Washington, University of Tokyo, Saarland University, and NTT Communication Science Laboratories. His research interests include text mining of social media, deep learning, computational lexical semantics, information extraction, and web mining. Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of Technology in 1998 and 2001, respectively. Prior to commencing his current position at The University of Melbourne, he was a Senior Research Engineer at the Center for the Study of Language and Information, Stanford University (2001-2004).

Recent publications

  1. Hamzei, E.; Li, H.; Vasardani, M.; Baldwin, T.; Winter, S.; Tomko, M. Place questions and human-generated answers: A data analysis approach. Lecture Notes in Geoinformation and Cartography. Springer, Cham. 2020, pp. 3-19. DOI: 10.1007/978-3-030-14745-7_1
  2. Shen, A.; Salehi, B.; Baldwin, T.; Qi, J. A joint model for multimodal document quality assessment. 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL). IEEE. 2019, Vol. 2019-June, pp. 107-110. DOI: 10.1109/JCDL.2019.00024
  3. Mccaughey, T.; Budden, DM.; Sanfilippo, PG.; Gooden, GEC.; Fan, L.; Fenwick, E.; Rees, G.; Macgregor, C.; Si, L.; Chen, C.; Liang, HH.; Pebay, A.; Baldwin, T.; Hewitt, AW. A Need for Better Understanding Is the Major Determinant for Public Perceptions of Human Gene Editing. Human Gene Therapy. Mary Ann Liebert. 2019, Vol. 30, Issue 1, pp. 36-43. DOI: 10.1089/hum.2018.033
  4. Jauhiainen, T.; Lui, M.; Zampieri, M.; Baldwin, T.; Linden, K. Automatic Language Identification in Texts: A Survey. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH. AI ACCESS FOUNDATION. 2019, Vol. 65, pp. 675-782. DOI: 10.1613/jair.1.11675
  5. Vylomova, E.; Cotterell, R.; Baldwin, T.; Cohn, T.; Eisner, J. Contextualization of Morphological Inflection. CoRR. Association for Computational Linguistics. 2019, Vol. abs/1905.01420, pp. 2018-2024.
  6. Nandakumar, N.; Baldwin, T.; Salehi, B. How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions. Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for. Association for Computational Linguistics. 2019. DOI: 10.18653/v1/w19-2004
  7. Shivashankar, S.; Baldwin, T.; Brooke, J.; Cohn, T. Pairwise webpage coreference classification using distant supervision. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3-7, 2017. ACM Press. 2019, pp. 841-842. DOI: 10.1145/3041021.3054224
  8. Mathur, N.; Baldwin, T.; Cohn, T. Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. 2019, pp. 2799-2808. DOI: 10.18653/v1/P19-1269
  9. Li, Y.; Baldwin, T.; Cohn, T. Semi-supervised Stochastic Multi-Domain Learning using Variational Inference. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. 2019, pp. 1923-1934. DOI: 10.18653/v1/P19-1186
  10. Subramanian, S.; Cohn, T.; Baldwin, T. Target Based Speech Act Classification in Political Campaign Text. Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*. Association for Computational Linguistics. 2019. DOI: 10.18653/v1/s19-1030
  11. Li, H.; Wang, M.; Baldwin, T.; Tomko, M.; Vasardani, M. UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution. Proceedings of the 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics. 2019, pp. 1313-1318. DOI: 10.18653/v1/s19-2231
  12. Hur, B.; Hardefeldt, LY.; Verspoor, K.; Baldwin, T.; Gilkerson, JR. Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia. Australian Veterinary Journal. Wiley. 2019, Vol. 97, Issue 8, pp. 298-300. DOI: 10.1111/avj.12836
  13. Salehi, B.; Spina, D.; Moffat, A.; Sadeghi, S.; Scholer, F.; Baldwin, T.; Cavedon, L.; Sanderson, M.; Wong, W.; Zobel, J. A living lab study of query amendment in job search. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18. ACM. 2018, pp. 905-908. DOI: 10.1145/3209978.3210082
  14. Spina, D.; Maistro, M.; Ren, Y.; Sadeghi, S.; Wong, W.; Baldwin, T.; Cavedon, L.; Moffat, A.; Sanderson, M.; Scholer, F.; Zobel, J. A preliminary comparison of job, talent, and web search. CEUR Workshop Proceedings. CEUR Workshop Proceedings. 2018, Vol. 2140.
  15. Subramanian, S.; Baldwin, T.; Cohn, T. Content-based popularity prediction of online petitions using a deep regression model. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 2: Short Papers. ACL Anthology. 2018, Vol. 2, pp. 182-188.
  16. 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.
  17. Hoogeveen, D.; Bennett, A.; Li, Y.; Verspoor, KM.; Baldwin, T. Detecting misflagged duplicate questions in community question-answering archives. 12th International AAAI Conference on Web and Social Media, ICWSM 2018. AAAI Press. 2018, pp. 112-120.
  18. Subramanian, S.; Cohn, T.; Baldwin, T. Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers). Association for Computational Linguistics. 2018, pp. 1964-1974.
  19. Salehi, B.; Liu, F.; Baldwin, T.; Wong, W. Multitask learning for query segmentation in job search. Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval - ICTIR '18. ACM Press. 2018, pp. 179-182. DOI: 10.1145/3234944.3234965
  20. Liu, F.; Cohn, T.; Baldwin, T. Narrative Modeling with Memory Chains and Semantic Supervision. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). ACL Anthology. 2018, Vol. 2, pp. 278-284.

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