From POS tagging to dependency parsing for biomedical event extraction

11 Aug 2018 Dat Quoc Nguyen Karin Verspoor

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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Dependency Parsing GENIA - LAS BiLSTM-CRF F1 91.92 # 1
Dependency Parsing GENIA - UAS BiLSTM-CRF F1 92.84 # 1

Methods used in the Paper


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