Representation and learning in information retrieval
Representation and learning in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A new differential LSI space-based probabilistic document classifier
Information Processing Letters
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic scoring of non-native spontaneous speech in tests of spoken English
Speech Communication
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Boosting for text classification with semantic features
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Communications of the ACM
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This paper presents a thesis proposal on approaches to automatically scoring non-native speech from second language tests. Current speech scoring systems assess speech by primarily using acoustic features such as fluency and pronunciation; however content features are barely involved. Motivated by this limitation, the study aims to investigate the use of content features in speech scoring systems. For content features, a central question is how speech content can be represented in appropriate means to facilitate automated speech scoring. The study proposes using ontology-based representation to perform concept level representation on speech transcripts, and furthermore the content features computed from ontology-based representation may facilitate speech scoring. One baseline and two ontology-based representations are compared in experiments. Preliminary results show that ontology-based representation slightly improves performance of one content feature for automated scoring over the baseline system.