Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Journal of Intelligent Information Systems
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The ESA retrieval model revisited
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Concept-based feature generation and selection for information retrieval
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words
Natural Language Engineering
A Wikipedia-based multilingual retrieval model
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Evaluating cross-language explicit semantic analysis and cross querying
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
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Semantic analysis tries to solve problems arising from polysemy and synonymy that are abundant in natural languages. Recently, Gabrilovich and Markovitch propose the Explicit Semantic Analysis (ESA) technique, which complements the well-known Latent Semantic Analysis (LSA) technique. In this paper, we show that the two techniques are not as distinct as their names suggest; instead, we find that ESA is equivalent to a LSA variant, and this equivalence generalizes to all kernel methods using kernels arising from the canonical dot product. Effectively, this result guarantees that ESA would not outperform the peak efficacy of LSA for any applications using the above kernel methods. In short, this paper for the first time establishes the connections between ESA and LSA, quantifies their relative efficacy, and generalizes the result to a big category of kernel methods.