MayoNLP at SemEval-2016 task 1: Semantic textual similarity based on lexical semantic net and deep learning semantic model

Abstract

Given two sentences, participating systems assign a semantic similarity score in the range of 0-5. We applied two different techniques for the task: one is based on lexical semantic net (corresponding to run 1) and the other is based on deep learning semantic model (corresponding to run 2). We also combined these two runs linearly (corresponding to run 3). Our results indicate that the two techniques perform comparably while the combination outperforms the individual ones on four out of five datasets, namely answer-answer, headlines, plagiarism, and question-question, and on the overall weighted mean of STS 2016 and 2015 datasets.