Exploiting Multi-Features to Detect Hedges and their Scope in Biomedical Texts

In this paper, we present a machine learning approach that detects hedge cues and their scope in biomedical texts. Identifying hedged information in texts is a kind of semantic filtering of texts and it is important since it could extract speculative information from factual information. In order to deal with the semantic analysis problem, various evidential features are proposed and integrated through a Conditional Random Fields (CRFs) model. Hedge cues that appear in the training dataset are regarded as keywords and employed as an important feature in hedge cue identification system. For the scope finding, we construct a CRF-based system and a syntactic pattern-based system, and compare their performances. Experiments using test data from CoNLL-2010 shared task show that our proposed method is robust. F-score of the biological hedge detection task and scope finding task achieves 86.32% and 54.18% in in-domain evaluations respectively.