Abstract
In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus. The goal of this effort is to create a lexical resource for German causal relations. The resource will consist of a lexicon that describes constructions that trigger causality as well as the participants of the causal event, and will be augmented by a corpus with annotated instances for each entry, that can be used as training data to develop a system for automatic classification of causal relations. Focusing on verbs, our method harvested a set of 100 different lexical triggers of causality, including support verb constructions. At the moment, our corpus includes over 1,000 annotated instances. The lexicon and the annotated data will be made available to the research community.- Anthology ID:
- W17-0813
- Volume:
- Proceedings of the 11th Linguistic Annotation Workshop
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Nathan Schneider, Nianwen Xue
- Venue:
- LAW
- SIG:
- SIGANN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 105–114
- Language:
- URL:
- https://aclanthology.org/W17-0813
- DOI:
- 10.18653/v1/W17-0813
- Cite (ACL):
- Ines Rehbein and Josef Ruppenhofer. 2017. Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants. In Proceedings of the 11th Linguistic Annotation Workshop, pages 105–114, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants (Rehbein & Ruppenhofer, LAW 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W17-0813.pdf