Abstract
Causal relations play a key role in information extraction and reasoning. Most of the times, their expression is ambiguous or implicit, i.e. without signals in the text. This makes their identification challenging. We aim to improve their identification by implementing a Feedforward Neural Network with a novel set of features for this task. In particular, these are based on the position of event mentions and the semantics of events and participants. The resulting classifier outperforms strong baselines on two datasets (the Penn Discourse Treebank and the CSTNews corpus) annotated with different schemes and containing examples in two languages, English and Portuguese. This result demonstrates the importance of events for identifying discourse relations.- Anthology ID:
- W17-0903
- Volume:
- Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Michael Roth, Nasrin Mostafazadeh, Nathanael Chambers, Annie Louis
- Venue:
- LSDSem
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–30
- Language:
- URL:
- https://aclanthology.org/W17-0903
- DOI:
- 10.18653/v1/W17-0903
- Cite (ACL):
- Edoardo Maria Ponti and Anna Korhonen. 2017. Event-Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 25–30, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Event-Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse (Ponti & Korhonen, LSDSem 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W17-0903.pdf