Chinese abbreviation-definition identification: A SVM approach using context information

Abstract

As a special form of unknown words, Chinese abbreviations represent significant problems for Chinese text processing. The goal of this study is to automatically find the definition for a Chinese abbreviation in the context where both the abbreviation and its definition occur, enforcing the constraint of one sense per discourse for an abbreviation. First, the candidate abbreviation-definition pairs are collected, and then a SVM approach using context information is employed to classify candidate abbreviation-definition pairs so that the pairs can be identified. The performance of the approach is evaluated on a manually annotated test corpus, and is also compared with two other machine learning approaches: Maximum Entropy and Decision Tree. Experimental results show that our approach reaches a good performance.