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It is important to find out interactive links between pairs of utterances in multi-party conversation like an online chat. Though the usage of linguistic information is necessary to do this, we showed the better performance to this criterion by using physical meta-information that consists of the number of conversation members, the distance between utterances, and the frequency of individual utterance. The result of the examination of Decision Tree learning showed the accuracy is 84.1%, the precision is 87.1% and the recall is 66.9% for link between same speaker’s utterances, and the accuracy is 77.5%, the precision is 68.5% and the recall is 56.8% for link between others’ utterances. The result of the examination without metainformation showed the accuracy is 62.7%, the precision is 86.7%, the recall is 64.5% for same speaker’s utterances, and the accuracy is 77.9%, the precision is 73.8% and the recall is 49.9% for others’. These results showed we could find new links by using meta-information.

A SYSTEM OF LEARNING LINKS BETWEEN UTTERANCES WITH META-INFORMATION IN CHARACTER BASED MULTI-PARTY CONVERSATION BY DECISION TREE