Akiko Murakami

Term aggregation: mining synonymous expressions using personal stylistic variations

We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. This paper proposes a new methodology to improve the accuracy of a term aggregation system using each authors text as a coherent corpus. Our approach is based on the idea that one person tends to use one expression for one meaning. According to our assumption, most of the words with similar context features in each authors corpus tend not to be synonymous expressions. Our proposed method improves the accuracy of our term aggregation system, showing that our approach is successful.

Multi-topical discussion summarization using structured lexical chains and cue words

We propose a method to summarize threaded, multi-topical texts automatically, particularly online discussions and e-mail conversations. These corpora have a so-called reply-to structure among the posts, where multiple topics are discussed simultaneously with a certain level of continuity, although each post is typically short. We specifically focus on the multi-topical aspect of the corpora, and propose the use of two linguistically motivated features: lexical chains and cue words, which capture the topics and topic structure. Particularly, we introduce the structured lexical chain, which is a combination of traditional lexical chains with the thread structure. In experiments, we show the effectiveness of these features on the Innovation Jam 2008 Corpus and the BC3 Mailing List Corpus based on two task settings: key-sentence and keyword extraction. We also present detailed analysis of the result with some intuitive examples.