Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter
part. This part of the analysis multiplies a large matrix by a term vector to produce a high …

Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness
between terms or documents based on similarities to documents of a references corpus …

Abstract Explicit Semantic Analysis (ESA) is an approach to calculate the semantic
relatedness between two words or natural language texts with the help of concepts …

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 …

Since its debut the Explicit Semantic Analysis (ESA) has received much attention in the IR
community. ESA has been proven to perform surprisingly well in several tasks and in …

Computing semantic relatedness of natural language texts requires access to vast amounts
of common-sense and domain-specific world knowledge. We propose Explicit Semantic …

Computing the semantic relatedness between two words or phrases is an important problem
for fields such as information retrieval and natural language processing. One state-of-the-art …

In recent years, the Explicit Semantic Analysis (ESA) method has got a good performance in
computing semantic relatedness (SR). However, ESA method has failed to consider the …

Computing the semantic relatedness between two words or phrases is an important problem
in fields such as information retrieval and natural language processing. Explicit Semantic …

Abstract Explicit Semantic Analysis (ESA) utilizes the Wikipedia knowledge base to
represent the semantics of a word by a vector where every dimension refers to an explicitly …