Exploiting semantic roles for asynchronous question answering in an educational setting

Dunwei Wen, John Cuzzola, Lorna Brown, Kinshuk

    Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

    2 Citations (Scopus)

    Abstract

    Recent question answering (QA) research has started to incorporate deep natural language processing (NLP) such as syntactic and semantic parsing in order to enhance the capability of selecting the most relevant answers to a given question. However, current NLP technology involves intensive computing and thus hard to meet the real-time demand of synchronous QA. To improve e-learning we introduce NLP into a QA system that specifically exploits the communication latency between student and instructor. We present how the system will fit for educational environment, and how semantic similarity matching between a question and its candidate answers can be improved by semantic roles. The designed system and its running results show the perspective and potential of this research.

    Original languageEnglish
    Title of host publicationAdvances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings
    Pages374-379
    Number of pages6
    DOIs
    Publication statusPublished - 2012
    Event25th Canadian Conference on Artificial Intelligence, AI 2012 - Toronto, ON, Canada
    Duration: 28 May 201230 May 2012

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7310 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference25th Canadian Conference on Artificial Intelligence, AI 2012
    Country/TerritoryCanada
    CityToronto, ON
    Period28/05/1230/05/12

    Keywords

    • asynchronous QA
    • e-learning
    • natural language processing
    • question answering
    • semantic role labeling

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