Overview of Chapter 6: Discourse and Dialogue. By Professor Barbara Grosz, Harvard University. In Survey of the State of the Art in Human Language Technology (1996). Editorial Board: Ronald A. Cole, Editor in Chief, Joseph Mariani, Hans Uszkoreit, Annie Zaenen, Victor Zue; Managing Editors: Giovanni Battista Varile, Antonio Zampolli. "The problems addressed in discourse research aim to answer two general kinds of questions: (1) what information is contained in extended sequences of utterances that goes beyond the meaning of the individual utterances themselves? (2) how does the context in which an utterance is used affect the meaning of the individual utterances, or parts of them?"

Also see Professor Grosz's home page at Harvard University: "Professor Grosz has developed a theory of discourse structure that specifies how discourse interpretation depends on interactions among speaker intentions, attentional state, and linguistic form. Her current research in discourse processing has two foci. First, with colleagues at AT&T Bell Laboratories, she is using the theory to study the information about discourse structure conveyed by intonation, i.e., how tones demark, in spoken language, some of the structure that paragraphs and parentheses indicate in written language. Applications of this work should lead to better computer speech-synthesis systems. Second, she is involved in an interdisciplinary investigation of the connections between centering of attention and form of reference. These two strands of research are being combined in an effort (joint with Professor Stuart Shieber) that aims to provide the scientific and technological base for a new paradigm for human-computer interaction, one that would enable the principled design of multi-modal dialogue-supporting interfaces."

Investigations in Natural Language Generation. By Johanna D. Moore. "Analyses of human-human interactions indicate two crucial features that distinguish human explanations from those of their computational counterparts. First, humans structure their discourse and use linguistic cues to convey this structure to their audience. Second, human dialogue participants freely refer to the context created by the ongoing conversation."

Two Thumbs Up. By Leah Hoffmann. Forbes.com. (November 15, 2005) "'Sentiment analysis,' as the field of research is known, is a hot topic among computer scientists these days. The goal is to create computer programs that can determine whether a document is positive or negative. ... Successful applications could help automate market and product research and dramatically alter the future of a simple Internet search. ... 'The variety of words that people use for subjective expressions is staggering,' says Janyce Wiebe, a professor of computer science at the University of Pittsburgh. Wiebe and her colleagues have already assembled a dictionary of some 8,000 indicator words and phrases. 'The dictionary tells you whether a word is positive or negative when it's taken out of context,' Wiebe explains. 'The challenge is to figure out whether it's positive or negative in each individual instance.' There are a number of different ways to accomplish this. ... Fast Search and Transfer ASA ... unveiled a customizable sentiment analysis program, Marketrac, last year. ... Other potential applications in the field of sentiment analysis include automated flame detectors for online bulletin boards, tracking systems for stock market reports and programs that monitor movie or product reviews."

Point of view and discourse processing. By Janyce M. Wiebe. Working Notes of the 1991 AAAI Fall Symposium on Discourse Structure in Natural Language Understanding and Generation, pp. 136-137. Paper (short) or Transparencies (more complete). "To determine the current [point of view], readers cannot consider sentences in isolation, because most subjective sentences are not explicitly marked for point of view. However, there are regularities in the ways that authors continue, resume, and initiate characters' points of view."

• Also by this author: Learning Subjective Adjectives from Corpora. Proc. 2000. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, 735 - . Menlo Park, Calif.: AAAI Press. Postscript version. PDF version. "Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval."

Computational Models of Discourse - Instructor:Professor Regina Barzilay; MIT OpenCourseWare (Spring 2004). "This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing.The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing.This subject qualifies as an Artificial Intelligence and Applications concentration subject."

Discourse and Inference: Magnum Opus in Progress. By Jerry R. Hobbs, Information Sciences Institute, University of Southern California. Excerpt from The Problems of Discourse (Chapter 1 subsection): "From one point of view, the point of view of daily life, all of these problems are not really problems. How do we solve them? We just do. But if we ask about the mechanisms that underlie our ability to solve these problems, and we are very strict about what we mean by 'mechanism', all of these become significant problems indeed.For a number of years, researchers in the fields of artificial intelligence (AI) and cognitive psychology have looked at discourse from just such a point of view. The theory developed in this book arises out of this AI tradition. Workers in AI have tried to build natural language understanding systems, and the computer has placed quite stringent limits on what counts as a mechanism. This work is of more than just technological relevance. Today, only people understand language. Efforts to get computers to understand language must therefore draw on what is known about how people do it. Conversely, in spite of the substantial differences in architecture and power between the human brain and the present-day computer, there is embedded within most natural language processing systems a theory, implicit or explicit, of how language would be comprehended by any intelligent entity, hence by humans. If a theory of discourse is framed at a sufficiently abstract level, it may apply equally well to human and computer understanding.The fundamental lesson so far of this AI research is that we understand discourse so well because we know so much. We understood the Sapir text because we know a lot about speech, familiarity, features, daily life, and so on. But we do not just have the knowledge, we are able to use the knowledge to make sense of the text. The central problems in understanding how people interpret discourse are therefore how to characterize the knowledge that people have and the processes they use to deploy that knowledge in the task of interpreting discourse. It is the aim of this book to address these two problems."

• Also see his Publications in Discourse Analysis.

SIGdial, the Special Interest Group on Discourse and Dialogue: "SIGdial is a Special Interest Group (SIG) of the Association for Computational Linguistics (ACL) and of the International Speech Communication Association(ISCA). Resources include:

Related Pages

• Common Sense
• Interfaces
• Natural Language
• NL Understanding & Generation

Other References Offline

Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays. By Jill Burstein, Daniel Marcu, Kevin Knight. IEEE Intelligent Systems January/February 2003 (Vol. 18, No. 1). "Automated essay-scoring applications are widely used from the elementary-school through university levels for large-scale assessment and classroom instruction. This goes hand in hand with the increase of essay writing on standardized tests. Writing teachers show growing excitement about the innovative automated-essay-evaluation software that helps students improve their writing. Integration of this software into the curriculum is also consistent with the drive toward individualized assessment and instruction. One kind of application developed for this purpose is an essay-based discourse analysis system. This software shows students the presence and absence of relevant essay-based discourse elements in their essays, including introductory material, thesis statements, main ideas, supporting ideas, and conclusions."

Software referees group calls. By Kimberly Patch. Technology Research News (June 18/25, 2003). "The researchers tapped a sociological discipline -- conversation analysis -- to find ways to automatically tell who is talking to whom. ... Conversation analysts review examples of human interaction in order to understand how these practices work. The researchers quantified speech patterns gleaned by conversation analysts that generally show whether or not people are in conversation, and built software that determines what grouping of people is supported by the best evidence."