Explicit Semantic Analysis (ESA) is an approach to calculate the semantic relatedness between two words or natural language texts with the help of concepts grounded in human cognition. ESA usage has received much attention in the field of natural language processing, information retrieval and text analysis, however, performance of the approach depends on several parameters that are included in the model, and also on the text data type used for evaluation. In this paper, we investigate the behavior of using different number of Wikipedia articles in building ESA model, for calculating the semantic relatedness for different types of text pairs: word-word, phrasephrase and document-document. With our findings, we further propose an approach to improve the ESA semantic relatedness scores for words by enriching the words with their explicit context such as synonyms, glosses and Wikipedia definitions.
['Nitish Aggarwal', 'Kartik Asooja', 'Paul Buitelaar']
Exploring ESA to Improve Word Relatedness