Open AccessA Semi-Supervised Generative Model Integrating Both Syntactic and Semantic Features for Bacterial Subcellular Localization Extraction
Author(s) -
Zhongmin Shi,
Zhong Li,
Guishi Lin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1607/1/012115
Subject(s) - generative grammar , computer science , artificial intelligence , sentence , natural language processing , generative model , set (abstract data type) , relationship extraction , information extraction , programming language
Our study on the Bacterial Subcellular Localizations (BPLs) extraction 1 focuses on generative learning. We propose a generative model extracting BPLs from MEDLINE abstracts. The model integrates both syntactic and semantic features of a sentence, and capable of identifies biomedical named-entities and relations at the same time from a large set of noisy biomedical data. The overall performance of the model exhibits a significant improvement comparing to a supervised alternative.