Employing Event Inference to Improve Semi-Supervised Chinese Event Extraction
Peifeng Li, Qiaoming Zhu, Guodong Zhou · 2014
Although semi-supervised model can extract the event mentions matching frequent event patterns, it suf-fers much from those event mentions, which match infrequent patterns or have no matching pattern. To solve this issue, this paper introduces various kinds of linguistic knowledge-driven event inference mechanisms to semi-supervised Chinese event extraction. These event inference mechanisms can capture linguistic knowledge from four aspects, i.e. semantics of argument role, compositional semantics of trig-ger, consistency on coreference events and relevant events, to further recover missing event mentions from unlabeled texts. Evaluation on the ACE 2005 Chinese corpus shows that our event inference mech-anisms significantly outperform the refined state-of-the-art semi-supervised Chinese event extraction system in F1-score by 8.5%. 1