A Word Extraction Method from Sequential Text Data Based on Time Dependence Between Word Occuuerrences

Abstract

This paper shows a new method of extracting important words from newspaper corpus based on the temporal-dependency between word occurrences. This word extraction method plays an important role in event-sequence mining. TF･IDF is a well-known method to rank word's importance in a document. We already proposed a new word-extraction method of improving TFIDF method, called TF･IDayF,which considers temporal information of word occurrences and can extract important/characteristic words of expressing sequential events. However, this method does not consider any temporal dependency of word occurrences, which can be regarded as some causal relationships. In this paper, we propose a novel method for extracting important words by using temporal co-occurrence information of words in a newspaper corpus.