A tree-like structured perceptron for transition-based biomedical event extraction

Abstract

Event extraction in the biomedical domain has many complex situations, such as nested events, overlapping events, and multiple processing streams. For the problem of massive nested and multi-level nested events in the corpus, existing approaches generally transform these nested events into judgments of the relationships between triggers, resulting ineffective utilization of the complete event information. Similarly, when confronted with the issue of overlapping events in the corpus, the prevailing approach often recognizes only one of the overlapping events while neglecting the other. Due to the complexity of the biomedical events, their effectiveness is unsatisfactory. Biomedical events, which contain nested and overlapping events, can be conceptualized as a tree structure. In light of this, we present a novel tree-like structured perceptron designed for transition-based biomedical event extraction, which converts event predictions into sequences of transition operations. This approach enables the incremental construction of nested events, with each operation relying on the already constructed event and surrounding contextual features. We evaluate the proposed model on two tasks: Genia 2011 and Genia 2013. The experimental results show a 0.22% and 0.04% improvement in F1 score, respectively, compared to the currently better performing baseline models without using external resources.