From POS tagging to dependency parsing for biomedical event extraction
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Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance. In this paper, we perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core NLP tasks of POS tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. We also perform a task-oriented evaluation to investigate the influences of these models in a downstream application on biomedical event extraction.
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