Abstract
Incorporating domain knowledge is vital in building successful natural language processing (NLP) applications. Many times, cross-domain application of a tool results in poor performance as the tool does not account for domain-specific attributes. The clinical domain is challenging in this aspect due to specialized medical terms and nomenclature, shorthand notation, fragmented text, and a variety of writing styles used by different medical units. Temporal resolution is an NLP task that, in general, is domain-agnostic because temporal information is represented using a limited lexicon. However, domain-specific aspects of temporal resolution are present in clinical texts. Here we explore parsing issues that arose when running our system, a tool built on Newswire text, on clinical notes in the THYME corpus. Many parsing issues were straightforward to correct; however, a few code changes resulted in a cascading series of parsing errors that had to be resolved before an improvement in performance was observed, revealing the complexity temporal resolution and rule-based parsing. Our system now outperforms current state-of-the-art systems on the THYME corpus with little change in its performance on Newswire texts.- Anthology ID:
- N19-1369
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3682–3692
- Language:
- URL:
- https://aclanthology.org/N19-1369
- DOI:
- 10.18653/v1/N19-1369
- Cite (ACL):
- Amy Olex, Luke Maffey, and Bridget McInnes. 2019. NLP Whack-A-Mole: Challenges in Cross-Domain Temporal Expression Extraction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3682–3692, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- NLP Whack-A-Mole: Challenges in Cross-Domain Temporal Expression Extraction (Olex et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1369.pdf