Abstract
Although there are many text classification techniques depending on vector spaces, it is difficult to detect the meaning relating to the user’s intention (complaint, encouragement, request, invitation, etc.). The intention to be discussed in this study is very useful for understanding focus points in conversation. This paper presents a technique of determining the speaker’s intention for sentences in conversation. The intention association expressions are introduced and the formal rule descriptions with weight using these expressions are defined to build intention classification knowledge. A deterministic multi-attribute pattern-matching algorithm is used to determine the intention class efficiently. From simulation results for 681 E-mail messages of 5,859 sentences, the multi-attribute pattern matching algorithm is about 44.5 times faster than Aho and Corasick method. The precision and recall of intention classification of sentences are 91%, 95%. Precision and recall of the classification of each mail are 88%, 89%.