A Multivariate Gaussian Mixture Model for Automatic Compound Word Extraction

An improved statistical model is proposed in this paper for extracting compound words from a text corpus. Traditional terminology extraction methods rely heavily on simple filtering-and-thresholding methods, which are unable to minimize the error counts objectively. Therefore, a method for minimizing the error counts is very desirable. In this paper, an improved statistical model is developed to integrate parts of speech information as well as other frequently used word association metrics to jointly optimize the extraction tasks. The features are modelled with a multivariate Gaussian mixture for handling the inter-feature correlations properly. With a training (resp. testing) corpus of 20715 (resp. 2301) sentences, the weighted precision & recall (WPR) can achieve about 84% for bigram compounds, and 86% for trigram compounds. The F-measure performances are about 82% for bigrams and 84% for trigrams.