Abstract
This paper describes a Natural language processing system developed for automatic identification of explicit connectives, its sense and arguments. Prior work has shown that the difference in usage of connectives across corpora affects the cross domain connective identification task negatively. Hence the development of domain specific discourse parser has become indispensable. Here, we present a corpus annotated with discourse relations on Medline abstracts. Kappa score is calculated to check the annotation quality of our corpus. The previous works on discourse analysis in bio-medical data have concentrated only on the identification of connectives and hence we have developed an end-end parser for connective and argument identification using Conditional Random Fields algorithm. The type and sub-type of the connective sense is also identified. The results obtained are encouraging.- Anthology ID:
- W16-5110
- Volume:
- Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Sophia Ananiadou, Riza Batista-Navarro, Kevin Bretonnel Cohen, Dina Demner-Fushman, Paul Thompson
- Venue:
- WS
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 89–98
- Language:
- URL:
- https://aclanthology.org/W16-5110
- DOI:
- Cite (ACL):
- Sindhuja Gopalan and Sobha Lalitha Devi. 2016. BioDCA Identifier: A System for Automatic Identification of Discourse Connective and Arguments from Biomedical Text. In Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016), pages 89–98, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- BioDCA Identifier: A System for Automatic Identification of Discourse Connective and Arguments from Biomedical Text (Gopalan & Lalitha Devi, 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W16-5110.pdf