Author: John Niekrasz
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Meeting Structure Annotation
We describe a generic set of tools for representing, annotating, and analysing multi-party discourse, including: an ontology of multimodal discourse, a programming interface for that ontology, and NOMOS – a flexible and extensible toolkit for browsing and annotating discourse.
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Resolving ”You” in Multi-Party Dialog
This paper presents experiments into the resolution of “you” in multi-party dialog, dividing this process into two tasks: distinguishing between generic and referential uses; and then, for referential uses, identifying the referred-to addressee(s).
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Detecting and Summarizing Action Items in Multi-Party Dialogue
This paper addresses the problem of identifying action items discussed in open-domain conversational speech, and does so in two stages: firstly, detecting the subdialogues in which action items are proposed, discussed and committed to; and secondly, extracting the phrases that accurately capture or summarize the tasks they involve.
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A meeting browser that learns
We present a system for extracting useful information from multi-party meetings and presenting the results to users via a browser.
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A Case Study in Engineering a Knowledge Base for an Intelligent Personal Assistant
We present a case study in engineering a large knowledge base (KB) to meet the requirements of a personal assistant. We discuss our KB development methodology and the engineering challenges we faced in the process.
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NOMOS: A Semantic Web software framework for annotation of multimodal corpora
We present NOMOS, an open-source software framework for annotation, processing, and analysis of multimodal corpora.
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Detecting Action Items in Multi-party Meetings: Annotation and Initial Experiments
This paper presents the results of initial investigation and experiments into automatic action item detection from transcripts of multi-party human-human meetings.
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Shallow Discourse Structure for Action Item Detection
We investigated automatic action item detection from transcripts of multi-party meetings.
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A Multimodal Discourse Ontology for Meeting Understanding
In this paper, we present a multimodal discourse ontology that serves as a knowledge representation and annotation framework for the discourse understanding component of an artificial personal office assistant.
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Meeting Structure Annotation: Data and Tools
We present a set of annotations of hierarchical topic segmentations and action item sub-dialogues collected over 65 meetings from the ICSI and ISL meeting corpora, designed to support automatic meeting understanding and analysis.
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Collaborative and argumentative models of natural discussions
We report in this paper experiences and insights resulting from the first two years of work in two similar projects on meeting tracking and understanding. The projects are the DARPA-funded CALO project and the Swiss National research project IM2.
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Ontology-based multi-party meeting understanding
This paper describes current and planned research efforts towards developing multimodal discourse understanding for an automated personal office assistant.