From POS tagging to dependency parsing for biomedical event extraction

TL;DR

This paper empirically compares traditional and neural models for POS tagging and dependency parsing in biomedical texts, revealing neural models generally perform better but do not always enhance downstream event extraction.

Contribution

It provides a comprehensive comparison of syntactic processing models in biomedical NLP and analyzes their impact on biomedical event extraction performance.

Findings

Neural models outperform feature-based models on biomedical corpora.

Better parsing accuracy does not always lead to improved event extraction.

The study offers retrained models for future research.

Abstract

Background: 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. Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (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. Experimental results show that in general, the neural models outperform the feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We also perform a task-oriented evaluation to investigate the influences of these models in a downstream application on biomedical event extraction, and show that better intrinsic parsing performance does not always imply better extrinsic event extraction performance. Conclusion: We have presented a detailed empirical study comparing traditional feature-based and neural network-based models for POS tagging and dependency parsing in the biomedical context, and also investigated the influence of parser selection for a biomedical event extraction downstream task. Availability of data and material: We make the retrained models available at https://github.com/datquocnguyen/BioPosDep