A hybrid method based on WordNet and Wikipedia for computing semantic relatedness between texts
|A hybrid method based on WordNet and Wikipedia for computing semantic relatedness between texts|
|Author(s)||Malekzadeh R., Bagherzadeh J., Noroozi A.|
|Published in||AISP 2012 - 16th CSI International Symposium on Artificial Intelligence and Signal Processing|
|Keyword(s)||information retrieval, lexical semantic knowledge, semantic relatedness, semantic similarity, Wikipedia, WordNet (Extra: Lexical semantics, Semantic relatedness, Semantic similarity, Wikipedia, Wordnet, Artificial intelligence, Information retrieval, Ontology, Semantics, Signal processing, Websites, Natural language processing systems)|
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A hybrid method based on WordNet and Wikipedia for computing semantic relatedness between texts is a 2012 conference paper written in English by Malekzadeh R., Bagherzadeh J., Noroozi A. and published in AISP 2012 - 16th CSI International Symposium on Artificial Intelligence and Signal Processing.
In this article we present a new method for computing semantic relatedness between texts. For this purpose we use a tow-phase approach. The first phase involves modeling document sentences as a matrix to compute semantic relatedness between sentences. In the second phase, we compare text relatedness by using the relation of their sentences. Since Semantic relation between words must be searched in lexical semantic knowledge source, selecting a suitable source is very important, so that produced accurate results with correct selection. In this work, we attempt to capture the semantic relatedness between texts with a more accuracy. For this purpose, we use a collection of tow well known knowledge bases namely, WordNet and Wikipedia, so that provide more complete data source for calculate the semantic relatedness with a more accuracy. We evaluate our approach by comparison with other existing techniques (on Lee datasets).
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