Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction

Chen Lin, Timothy A. Miller, Dmitriy Dligach, Hadi Amiri, Steven Bethard, Guergana Savova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance. We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework. Our framework makes use of labeled, unlabeled, and social media data, operates on basic features, and is scalable and generalizable. With this method, we establish the state-of-the-art result for both in- and cross-domain for a clinical temporal relation extraction task.

Original languageEnglish (US)
Title of host publicationEMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages165-176
Number of pages12
ISBN (Electronic)9781948087742
StatePublished - 2018
Event9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018 - Brussels, Belgium
Duration: Oct 31 2018 → …

Publication series

NameEMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop

Conference

Conference9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018
Country/TerritoryBelgium
CityBrussels
Period10/31/18 → …

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction'. Together they form a unique fingerprint.

Cite this