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AIC Seminar Series

Summarizing Spoken Conversations: The Importance of Modeling Joint Activity

John NiekraszHuman Communication Research Centre, The University of Edinburgh[Home Page]

Notice:  Job talk taking place at SRI San Diego. Room and WebEx session at Menlo Park, room TBA. Hosted by Bruce Harris.

Date:  2010-09-10 at 14:00

Location:  San Diego. Telecon at EJ228 (SRI E Building). Slides via WebEx from 1:45pm on, sound at 1-888-355-1249, 749045  (Directions)

   Abstract

Conversations are composed of episodes, each of which relate to a dominant communicative activity of the participants, such as giving instructions, telling a story, or making a group decision. Modeling these activities is important because they are part of participantsÂ’ commonsense understanding of what happens in a conversation. They appear in natural summaries of conversations such as meeting minutes, and participants talk about them within the conversation itself. Dominant joint activities reflect the overall purpose of communication and therefore represent an essential component of practical, common-sense descriptions of conversations.

From this starting point, I will present my research on *activity-oriented* summarization and information extraction for workplace meetings. The ultimate goal of this research is to develop methods for *abstraction* of conversations. My approach uses exclusively unsupervised statistical methods and is fundamentally based upon the exploitation of context for language understanding. I hypothesize that activities are principally indicated in language by expressions that establish a relationship between participants and the subject matter, e.g., subjective language, reference to participants, and deixis (context-dependent linguistic forms). In this talk, I will present results testing this hypothesis on two important problems in NLP: discourse segmentation and discourse segment labeling. Segmentation results show that my approach is effective at different levels and on different corpora, and competitive with state-of-the-art lexical-semantic approaches. The segment labeling experiments investigate algorithms that are the first to automatically label segments according to activity type, e.g., presentation, discussion, evaluation, in an unsupervised framework. I will also discuss some points of departure for future work on intentionality and activity as a contextual factor in automatic language understanding.

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