A transfer learning model with multi-source domains for biomedical event trigger extraction

摘要

Recently, with the biomedical research development, an explosive amount of literature has been published online. As a result, it has brought a big challenge to the tasks of biomedical Text Mining (TM) for automatic identification and tracking of the new discoveries and theories in these biomedical papers [1–3]. Recognizing biomedical events in text is one of critical tasks, which refers to automatically extracting structured representations of biomedical relations, functions and processes from text [3]. Since the BioNLP’09 [4] and BioNLP’11 [5] Shared Tasks, event extraction has become a research focus, and many biomedical event corpora have sprung up, especially on molecular-level. For instance, a corpus from the Shared Task (ST) of BioNLP’09 [4] contains 9 types of frequently used biomolecular events. A corpus from the Epigenetics and Post-translational Modifications (EPI) task of BioNLP’11 [5] contains 14 protein entity modification event types and their catalysis. And another corpus consists of events relevant to DNA methylation and demethylation and their regulations [6]. Moreover, in order to obtain a more comprehensive understanding of biological systems, the scope of event extraction must be broadened from molecular-level reactions to cellular-, tissue- and organ-level effects, and to organism-level outcomes [7]. Hence, in MLEE corpus [8] wide coverage of events from the molecular level to the whole organism have been annotated with 19 event categories.