A sentence classification technique using intention association expressions

Abstract

Although there are many text classification techniques depending on the vector space, it is difficult to detect the meaning related to the user’s intention (complaint, encouragement, request, invitation, etc.). The approach be discussed in this paper is very useful for understanding focus points in conversation. We present a technique for determining the speaker’s intention for sentences in conversation. Intention association expressions are introduced, and formal descriptions with weights are defined using these expressions to construct an intention classification. A deterministic multi-attribute pattern-matching algorithm is used to determine the intention class efficiently. In simulation results for 681 email messages of 5859 sentences, the multi-attribute pattern-matching algorithm is about 44.5 times faster than the Aho and Corasick method. The precision and recall of intention classification of sentences are 91% and 95%, respectively. The precision and recall of extraction of unnecessary sentences are 98% and 96%, respectively. The precision and recall of the classification of each email are 88% and 89%, respectively.