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Robust Semantic Text Similarity Using LSA, Machine Learning and Linguistic Resources

Authors: Abhay L. Kashyap, Lushan Han, Roberto Yus, Jennifer Sleeman, Taneeya W. Satyapanich, Sunil R Gandhi, and Tim Finin

Journal: Language Resources and Evaluation

Date: March 01, 2016

Abstract: Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM~2013~and SemEval-2014~tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of text. Additional wrappers and resources were used to handle task specific challenges that include processing Spanish text, comparing text sequences of different lengths, handling informal words and phrases, and matching words with sense definitions. In the *SEM~2013~task on Semantic Textual Similarity, our best performing system ranked first among the~89~submitted runs. In the SemEval-2014~task on Multilingual Semantic Textual Similarity, we ranked a close second in both the English and Spanish subtasks. In the SemEval-2014~task on Cross--Level Semantic Similarity, we ranked first in Sentence--Phrase, Phrase--Word, and Word--Sense subtasks and second in the Paragraph--Sentence subtask.

Type: Article

Publisher: Springer

Pages: 125-161

Number: 1

Volume: 50

Tags: natural language processing, word embedding, semantic similarity

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