Paper: Unsupervised Linguistically-Driven Reliable Dependency Parses Detection and Self-Training for Adaptation to the Biomedical Domain

ACL ID W13-1906
Title Unsupervised Linguistically-Driven Reliable Dependency Parses Detection and Self-Training for Adaptation to the Biomedical Domain
Venue Workshop on Biomedical Natural Language Processing
Session
Year 2013
Authors

In this paper, a new self?training method for domain adaptation is illustrated, where the selection of reliable parses is car- ried out by an unsupervised linguistically? driven algorithm, ULISSE. The method has been tested on biomedical texts with results showing a significant improve- ment with respect to considered baselines, which demonstrates its ability to capture both reliability of parses and domain? specificity of linguistic constructions.