From POS tagging to dependency parsing for biomedical event extraction
By Dat Quoc Nguyen, Karin M. Verspoor. 2018
36 citations recorded by the source corpus.
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. 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. 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. We make the retrained models available at https://github.com/datquocnguyen/BioPosDep.
Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kyle Lo
- Iz Beltagy
- Arman Cohan
- Mark Neumann
- Noah A. Smith
- Graham Neubig
- Daniel King
- Bridger Waleed Ammar
In Molecular Biology
- Kyle Lo
- Arman Cohan
- Iz Beltagy
- Mark Neumann
- Daniel King
- Bridger Waleed Ammar
- Daniel S. Weld
- M. V. Koroteev
In Natural Language Processing
- Kyle Lo
- Arman Cohan
- Iz Beltagy
- Mark Neumann
- Daniel King
- Bridger Waleed Ammar
- Daniel S. Weld
- Graham Neubig
Citations in the corpus
- SciBERT: A Pretrained Language Model for Scientific Text (2019, background): “In addition, SCIBERT achieves new SOTA results on BC5CDR and ChemProt (Lee et al., 2019), and EBM-NLP (Nye et al., 2018).”
- ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019, result): “We compare our models to the recent survey study of dependency parsing and POS tagging for biomedical data (Nguyen and Verspoor, 2018) in Tables 3 and 4.”
- SciBERT: Pretrained Contextualized Embeddings for Scientific Text (2019, unknown): “SCIBERT performs slightly worse than the SOTA on JNLPBA (Yoon et al., 2018), PubMed 20K RCT (Jin and Szolovits, 2018), and GENIA (Nguyen and Verspoor, 2019).”