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1
Utterance Classification in Auto Tutor
In: DTIC (2003)
Abstract: This paper describes classification of typed student utterances within AutoTutor, an intelligent tutoring system. Utterances are classified to one of 18 categories including 16 question categories. The classifier presented uses part of speech tagging, cascaded finite state transducers, and simple disambiguation rules. Shallow NLP is well suited to the task: session log file analysis reveals significant classification of eleven question categories, frozen expressions, and assertions. ; Sponsored in part by National Science Foundation Grant no. REC-0106965. Presented at the HLT-NAACL Workshop on Building Educational Applications Using Natural Language Processing held in Edmonton, Canada on 31 May 2003. Published in the Proceedings of the HLT-NAACL Workshop on Building Educational Applications Using Natural Language Processing, 2003.
Keyword: *AUTOTUTOR COMPUTER PROGRAM; *CLASSIFICATION; *NATURAL LANGUAGE; *STUDENTS; *UTTERANCE CLASSIFICATION; ALGORITHMS; CLASSIFIERS; COMPUTATIONAL LINGUISTICS; Computer Systems; Humanities and History; INFORMATION PROCESSING; Information Science; ITS(INTELLIGENT TUTORING SYSTEMS); Linguistics; QUESTION ANSWERING; SYMPOSIA; TRACKING
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA460937
http://www.dtic.mil/docs/citations/ADA460937
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2
The Benefit of Ontologies for Interoperability of CCIS. (Easy, Quick and Cheap Solutions are Impossible, if Semantics of CCIS are Affected.)
In: DTIC (2003)
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3
HowtogetaChineseName(Entity): Segmentation and Combination Issues
In: DTIC (2003)
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