Measuring Semantic Textual Similarity using modified Information Content of WordNet and Trigram Language Model
Goutam	Majumder, Partha	Pakray, David Eduardo Pinto Avendano
International Journal of Computational Linguistics Research, Vol. 8, No. 4, 2017.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v8n4/jclv8n4_3.pdf
Abstract:
The proposed method is developed for measuring the textual similarity using WordNet taxonomy and information
content. It uses the taxonomy knowledge and merge this information with an nâ€“gram based language model (n = 3). The proposed method considers WordNet synsets for lexical relationships between the words and language model for information content value between the two words of the different class. Finally, a similarity score is generated between two sentences by measuring maximum weight shortest path of the graph. To evaluate the system SemEval 2015 training dataset is considered and the high correlation coefficient of 0.885 has been achieved.