# An enhanced computational feature selection method for medical synonym identification via bilingualism and multi-corpus training

## TL;DR

This paper introduces an improved feature selection method for Chinese medical synonym identification, achieving high precision, recall, and F1 scores by combining bilingual and multi-corpus features.

## Contribution

The paper presents a novel feature selection approach that enhances Chinese medical synonym identification accuracy using bilingual and multi-corpus training.

## Key findings

- Achieved 97.37% precision rate
- Achieved 96.00% recall rate
- Achieved 97.33% F1 score

## Abstract

Medical synonym identification has been an important part of medical natural language processing (NLP). However, in the field of Chinese medical synonym identification, there are problems like low precision and low recall rate. To solve the problem, in this paper, we propose a method for identifying Chinese medical synonyms. We first selected 13 features including Chinese and English features. Then we studied the synonym identification results of each feature alone and different combinations of the features. Through the comparison among identification results, we present an optimal combination of features for Chinese medical synonym identification. Experiments show that our selected features have achieved 97.37% precision rate, 96.00% recall rate and 97.33% F1 score.

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Source: https://tomesphere.com/paper/1812.01879