A correlation-based semantic model for text search
|A correlation-based semantic model for text search|
|Author(s)||Sun J., Wang B., Yang X.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||semantic correlation, text search, Wikipedia (Extra: Information management, Exponential growth, Inaccurate rankings, Semantic Model, Text representation, Text representation models, Text search, Text similarity, Wikipedia, Semantics)|
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A correlation-based semantic model for text search is a 2014 conference paper written in English by Sun J., Wang B., Yang X. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
With the exponential growth of texts on the Internet, text search is considered a crucial problem in many fields. Most of the traditional text search approaches are based on "bag of words" text representation based on frequency statics. However, these approaches ignore the semantic correlation of words in the text. So this may lead to inaccurate ranking of the search results. In this paper, we propose a new Wikipedia-based similar text search approach that the words in the texts and query text could be semantic correlated in Wikipedia. We propose a new text representation model and a new text similarity metric. Finally, the experiments on the real dataset demonstrate the high precision, recall and efficiency of our approach.
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