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
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Intelligent Information Systems
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Wikipedia-Based Kernels for Text Categorization
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extended explicit semantic analysis for calculating semantic relatedness of web resources
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Insights into explicit semantic analysis
Proceedings of the 20th ACM international conference on Information and knowledge management
SemEval-2012 task 6: a pilot on semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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Explicit semantic analysis (ESA) is a technique for computing semantic relatedness between natural language texts. It is a document-based distributional model similar to latent semantic analysis (LSA), which is often built on the Wikipedia database when it is required for general English usage. Unlike LSA, however, ESA does not use dimensionality reduction, and therefore it is sometimes unable to account for similarity between words that do not co-occur with same concepts, even if their concepts themselves cover similar subjects. In the Wikipedia implementation ESA concepts are Wikipedia articles, and the Wikilinks between the articles are used to overcome the concept-similarity problem. In this paper, we provide two general solutions for integration of concept-concept similarities into the ESA model, ones that do not rely on a particular corpus structure and do not alter the explicit concept-mapping properties that distinguish ESA from models like LSA and latent Dirichlet allocation (LDA).