Extraction of Proper Names from Myanmar Text Using Latent Dirichlet Allocation

This paper proposes a method for proper names extraction from Myanmar text by using latent Dirichlet allocation (LDA). Our method aims to extract proper names that provide important information on the contents of Myanmar text. Our method consists of two steps. In the first step, we extract topic words from Myanmar news articles by using LDA. In the second step, we make a post-processing, because the resulting topic words contain some noisy words. Our post-processing, first of all, eliminates the topic words whose prefixes are Myanmar digits and suffixes are noun and verb particles. We then remove the duplicate words and discard the topic words that are contained in the existing dictionary. Consequently, we obtain the words as candidate of proper names, namely personal names, geographical names, unique object names, organization names, single event names, and so on. The evaluation is performed both from the subjective and quantitative perspectives. From the subjective perspective, we compare the accuracy of proper names extracted by our method with those extracted by latent semantic indexing (LSI) and rule-based method. It is shown that both LSI and our method can improve the accuracy of those obtained by rule-based method. However, our method can provide more interesting proper names than LSI. From the quantitative perspective, we use the extracted proper names as additional features in K-means clustering. The experimental results show that the document clusters given by our method are better than those given by LSI and rule-based method in precision, recall and F-score.