HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Saba Anwar, Dmitry Ustalov, Nikolay Arefyev, Simone Paolo Ponzetto, Chris Biemann, Alexander Panchenko
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 (Qasem-iZadeh 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.- Anthology ID:
- S19-2018
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 125–129
- Language:
- URL:
- https://aclanthology.org/S19-2018
- DOI:
- 10.18653/v1/S19-2018
- Cite (ACL):
- Saba Anwar, Dmitry Ustalov, Nikolay Arefyev, Simone Paolo Ponzetto, Chris Biemann, and Alexander Panchenko. 2019. HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 125–129, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings (Anwar et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-2018.pdf
- Code
- uhh-lt/semeval2019-hhmm
- Data
- FrameNet