Topic-informed neural approach for biomedical event extraction

Publication date: Available online 30 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Junchi Zhang, Mengchi Liu, Yue ZhangAbstractAs a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research