Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
Dongfang Li, Ying Xiong, Baotian Hu, Hanyang Du, Buzhou Tang, Qingcai Chen
Abstract
The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery. In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019. Task 1 can be regarded as the traditional name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. Task 2 can be regarded as relation extraction which captures the thematic roles between entities. For two tasks, we exploit the pre-trained biomedical language representation model (i.e., BERT) in the pipe of information extraction for the collection of mutation-disease knowledge from PubMed. And also, we design a fine-tuning technique and extra features by using multi-task learning. The experiment results show that our proposed approaches achieve 0.60 (ranks 1) and 0.25 (ranks 2) on task 1 and task 2 respectively in terms of F1 metric.- Anthology ID:
- D19-5711
- Volume:
- Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–76
- Language:
- URL:
- https://aclanthology.org/D19-5711
- DOI:
- 10.18653/v1/D19-5711
- Cite (ACL):
- Dongfang Li, Ying Xiong, Baotian Hu, Hanyang Du, Buzhou Tang, and Qingcai Chen. 2019. Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 72–76, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning (Li et al., BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D19-5711.pdf