Multiple Features Based Approach to Extract Bio-molecular Event Triggers Using Conditional Random Field

摘要

The purpose of Biomedical Natural Language Processing (BioNLP) is to capture biomedical phenomena from textual data by extracting relevant entities, information and relations between biomedical entities (i.e. proteins and genes). In general, in most of the published papers, only binary relations were extracted. In a recent past, the focus is shifted towards extracting more complex relations in the form of bio-molecular events that may include several entities or other relations. In this paper we propose an approach that enables event trigger extraction of relatively complex bio-molecular events. We approach this problem as a detection of bio-molecular event trigger using the well-known algorithm, namely Conditional Random Field (CRF). We apply our experiments on development set. It shows the overall average recall, precision and F-measure values of 64.27504%, 69.97559% and 67.00429%, respectively for the event detection.