Towards generalizable entity-centric clinical coreference resolution

Timothy Miller, Dmitriy Dligach, Steven Bethard, Chen Lin, Guergana Savova

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objective This work investigates the problem of clinical coreference resolution in a model that explicitly tracks entities, and aims to measure the performance of that model in both traditional in-domain train/test splits and cross-domain experiments that measure the generalizability of learned models. Methods The two methods we compare are a baseline mention-pair coreference system that operates over pairs of mentions with best-first conflict resolution and a mention-synchronous system that incrementally builds coreference chains. We develop new features that incorporate distributional semantics, discourse features, and entity attributes. We use two new coreference datasets with similar annotation guidelines – the THYME colon cancer dataset and the DeepPhe breast cancer dataset. Results The mention-synchronous system performs similarly on in-domain data but performs much better on new data. Part of speech tag features prove superior in feature generalizability experiments over other word representations. Our methods show generalization improvement but there is still a performance gap when testing in new domains. Discussion Generalizability of clinical NLP systems is important and under-studied, so future work should attempt to perform cross-domain and cross-institution evaluations and explicitly develop features and training regimens that favor generalizability. A performance-optimized version of the mention-synchronous system will be included in the open source Apache cTAKES software.

Original languageEnglish (US)
Pages (from-to)251-258
Number of pages8
JournalJournal of Biomedical Informatics
Volume69
DOIs
StatePublished - May 1 2017

Keywords

  • Clinical NLP
  • Coreference
  • Generalizability
  • Machine learning
  • Portability

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Fingerprint

Dive into the research topics of 'Towards generalizable entity-centric clinical coreference resolution'. Together they form a unique fingerprint.

Cite this