计算机科学
代表(政治)
背景(考古学)
自然语言处理
语义学(计算机科学)
编码(内存)
人工智能
领域(数学)
情报检索
程序设计语言
数学
古生物学
纯数学
法学
政治
生物
政治学
作者
Yongbing Xiao,Su-peng Liang,Jiang Peng,Zhijie Huang,Yan Wang,Jing Wang
出处
期刊:2021 International Conference on Computer Engineering and Application (ICCEA)
日期:2021-06-01
标识
DOI:10.1109/iccea53728.2021.00095
摘要
Distributed representations are usually used as input features in text mining tasks. Previous works have shown its potential in encoding semantics. Generally, there existing two representation methods namely static and dynamic, which means they are context-free and context-dependent respectively. Many works have demonstrated that context based representations significantly improved performance in natural language processing field. Therefore, in this paper, we utilize contextualized representations to recognize biomedical entities and evaluate the results at entity-level on BC2GM and BC5CDR-disease datasets. Results show that we obtain a F1-score of 75.16% and 75.97%, which improving 2.54% and 3.96% respectively compared with context-free representations. It indicates that the method based on contextualized representations is promising for entity recognition tasks.
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