Abstract: Text similarity plays an important role in natural language processing tasks such as answering questions and summarizing text. At present, state-of-the-art text similarity algorithms rely on inefficient word pairings and/or knowledge derived from large corpora such as Wikipedia. This article evaluates previous word similarity measures on benchmark datasets and then uses a hybrid word similarity in a novel text similarity measure (TSM). The proposed TSM is based on information content and WordNet semantic relations. TSM includes exact word match, the length of both sentences in a pair, and the maximum similarity between one word and the compared text. Compared with other well-known measures, results of TSM are surpassing or comparable with the best algorithms in the literature.

Issa Atoum and Ahmed Otoom, “Efficient Hybrid Semantic Text Similarity using Wordnet and a Corpus” International Journal of Advanced Computer Science and Applications(ijacsa), 7(9), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070917

@article{Atoum2016,
title = {Efficient Hybrid Semantic Text Similarity using Wordnet and a Corpus},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2016.070917},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070917},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {9},
author = {Issa Atoum and Ahmed Otoom},
}