Design for very large-scale conversations

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Includes bibliographical references (leaves 184-200).On the Internet there are now very large-scale conversations (VLSCs) in which hundreds, even thousands, of people exchange messages across international borders in daily, many-to-many communications. It is my thesis that VLSC is an emergent communication medium that engenders new social and linguistic connections between people. VLSC poses fundamental challenges to the analytic tools and descriptive methodologies of linguistics and sociology previously developed to understand conversations of a much smaller scale. Consequently, the challenge for software design is this: How can the tools of social science be appropriated and improved upon to create better interfaces for participants and interested observers to understand and critically reflect upon conversation? This dissertation accomplishes two pieces of work. Firstly, the design, implementation, and demonstration of a proof-of-concept, VLSC interface is presented. The Conversation Map system provides a means to explore and question the social and linguistic structure of very large-scale conversations (e.g., Usenet newsgroups). Secondly, the thinking that went into the design of the Conversation Map system is generalized and articulated as an aesthetics, ethics, and epistemology of design for VLSC. The goal of the second, theoretical portion of the thesis is to provide a means to describe the emergent phenomenon of VLSC and a vocabulary for critiquing software designed for VLSC and computer-mediated conversation in general.Warren Sack.Ph.D

WordNet-Based Inference of Textual Cohesion and Coherence

This paper 1 presents a computational method for the recognition of the cohesive and coherence structures of texts. A large lexical knowledge base built on top of WordNet provides with the lexico-semantic information that needs to be mined. A path-finding algorithm returns the cohesive structure of a text with results that outperform previous approaches. The lexical paths contained in the cohesive structures are used to (1) build patterns of association between cue phrases and coherence relations and (2) to find the lexical characteristics of coherence categories. Finally, the textual coherence structure is recognized by giving priority to the coherence constrains induced by cue phrases. The paper presents also the performance of building the coherence structure for several texts. Introduction In a text, a sequence of sentences tends to convey information about a certain topic, and by doing so, they use related words, providing the text with the quality of unity. This property of se..