Two-Character Chinese Word Extraction Based on Hybrid of Internal and Contextual Measures

Word extraction is one of the important tasks in text information processing. There are mainly two kinds of statistic-based measures for word extraction: the internal measure and the contextual measure. This paper discusses these two kinds of measures for Chinese word extraction. First, nine widely adopted internal measures are tested and compared on individual basis. Then various schemes of combining these measures are tried so as to improve the performance. Finally, the left/right entropy is integrated to see the effect of contextual measures. Genetic algorithm is explored to automatically adjust the weights of combination and thresholds. Experiments focusing on two-character Chinese word extraction show a promising result: the F-measure of mutual information, the most powerful internal measure, is 57.82%, whereas the best combination scheme of internal measures achieves the F-measure of 59.87%. With the integration of the contextual measure, the word extraction achieves the F-measure of 68.48% at last.