Error-Driven Boolean-Logic-Rule-Based Learning for Mining Chat-room Conversations

Abstract

The ephemeral nature of human communication via networks today poses interesting and challenging problems for information technologists. The sheer volume of communication in venues such as email, newsgroups, and chat precludes manual techniques of information management. Currently, no systematic mechanisms exist for accumulating these artifacts of communication in a form that lends itself to the construction of models of semantics [5]. In essence, dynamic techniques of analysis are needed if textual data of this nature is to be effectively mined. At Lehigh University we are developing a text mining tool for analysis of chat-room conversations. Project goals concentrate on the development of functionality to answer questions such as “What topics are being discussed in a chat-room?”, “Who is discussing which topics?” and “Who is interacting with whom?” The objective is to develop technology that can automatically identify such patterns of interaction in both social and semantic terms. In this article we present our preliminary findings for a novel technique developed to identify threads of conversation in multitopic, multi-person chat-rooms. This is the first step towards building models of social and semantic interaction. We term our technique Error-Driven Boolean-Logic- Rule-Based Learning (BLogRBL), a variation on Brill’s Transformation Based Learning [11] [12] [13]. Similar to Brill’s method, rules are automatically derived from templates during learning. It differs from Brill’s technique in that rules take the form of complex expressions of combinational logic. We report on the scope and design of our technique, as well as discussing preliminary results.