Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
WordNet: a lexical database for English
Communications of the ACM
The algorithm design manual
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Assigning function tags with a simple model
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
An optimal assessment of natural language student input using word-to-word similarity metrics
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Similarity measures based on latent dirichlet allocation
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
International Journal of Artificial Intelligence in Education
Experiments with semantic similarity measures based on LDA and LSA
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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This paper addresses the problem of evaluating students' answers in intelligent tutoring environments with mixed-initiative dialogue by modelling it as a textual entailment problem. The problem of meaning representation and inference is a pervasive challenge in any integrated intelligent system handling communication. For intelligent tutorial dialogue systems, we show that entailment cases can be detected at various dialog turns during a tutoring session. We report the performance of a lexico-syntactic approach on a set of entailment cases that were collected from a previous study we conducted with AutoTutor.