Although there are many text classification techniques depending on vector spaces, it is difficult to detect the meaning, which are relating to the user's intension (complaint, encouragement, request, invitation, etc.). The intension discussed in This work is very useful for understanding focus points in conversation. This paper presents a method of determining the speaker's intention for sentences in conversation. The intension association expressions are introduced, and the formal rule descriptions using these expressions are defined to build intention classification knowledge. A set pattern-matching algorithm is proposed to determine the intension class efficiently. From simulation results for 5,859 conversations, the presented set pattern-matching algorithm is about 44.5 times faster than Aho and Corasick method. Precision and recall of intension classifications are 90% and 95%. Moreover, precision and recall of unnecessary sentences extraction are 96% and 97%.