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Minh Ngoc Le

20152018
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Reinforcement learning Engineering & Materials Science
Taxonomies Engineering & Materials Science
semantics Social Sciences
learning method Social Sciences
Labeling Arts & Humanities
Semantic Roles Arts & Humanities
lack Social Sciences
learning Social Sciences

Research Output 2015 2018

  • 7 Conference contribution
  • 1 Poster
  • 1 Article

A Deep Dive into Word Sense Disambiguation with LSTM

Le, M. N., Postma, M. C., Urbani, J. & Vossen, P. T. J. M., 21 Aug 2018.

Research output: Contribution to ConferencePosterAcademic

Deep Dive into Word Sense Disambiguation with LSTM

Le, M. N., Postma, M. C., Urbani, J. & Vossen, P. T. J. M., 2018, Proceedings of the International Conference on Computational Linguistics (COLING 2018). International Conference on Computational Linguistics (COLING), p. 354-365 12 p.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Open Access

Neural Models of Selectional Preferences for Implicit Semantic Role Labeling

Le, M. N. & Fokkens, A. S., May 2018, Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC2018). Isahara, H., Maegaard, B., Piperidis, S., Cieri, C., Declerck, T., Hasida, K., Mazo, H., Choukri, K., Goggi, S., Mariani, J., Moreno, A., Calzolari, N., Odijk, J. & Tokunaga, T. (eds.). Miyazaki: European Language Resources Association (ELRA), p. 3062-3067 6 p. 624

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Open Access
semantics
learning method
Semantic Roles
Labeling
lack

Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing

Le, M. N. & Fokkens, A. S., Apr 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Valencia, Spain, Vol. 1. p. 677-687 10 p.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Reinforcement learning

Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion Words?

Le, M., Postma, M. & Urbani, J., 9 Dec 2017, In : arXiv.org.

Research output: Contribution to JournalArticleAcademic