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HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings (1905.01739v1)
Published 5 May 2019 in cs.CL
Abstract: We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.
- Saba Anwar (4 papers)
- Dmitry Ustalov (22 papers)
- Nikolay Arefyev (17 papers)
- Simone Paolo Ponzetto (52 papers)
- Chris Biemann (78 papers)
- Alexander Panchenko (92 papers)