We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature.
- Authors
Yuekun MA; He LIU; Dezheng ZHANG; Chang GAO; Yujue LIU
- Abstract
At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively.
- Subjects
CONVOLUTIONAL neural networks; CHINESE medicine; LEXICON; FEATURE extraction; ELECTRONIC publications; TRADITIONAL knowledge
- Publication
Technical Gazette / Tehnički Vjesnik, 2023, Vol 30, Issue 3, p899
- ISSN
1330-3651
- Publication type
Article
- DOI
10.17559/TV-20230121000257