Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) 2016
DOI: 10.18653/v1/s16-1111
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NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity

Abstract: This paper presents the submission of our team (NORMAS) to the SemEval 2016 semantic textual similarity (STS) shared task. We submitted three system runs, each using a set of 36 features extracted from the training set. The runs explore the use of the following three machine learning algorithms: Support Vector Regression, Elastic Net and Random Forest. Each run was trained using sentence pairs from the STS 2012 training data. Features extracted include lexical, syntactic and semantic features. This paper descr… Show more

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Cited by 2 publications
(1 citation statement)
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“…Most semantic processing methods only use part content of the corpus information. In this paper we hope to improve the accuracy of semantic similarity calculation by proposing a method to fuse the results of a) E-mail: duan jianyong@163.com b) E-mail: wuyuwei1996@foxmail.com c) E-mail: wuml@ncut.edu.cn d) E-mail: wanghaomails@gmail.com DOI: 10.1587/transinf.2019EDP7083 multiple models [3]- [5].…”
Section: Introductionmentioning
confidence: 99%
“…Most semantic processing methods only use part content of the corpus information. In this paper we hope to improve the accuracy of semantic similarity calculation by proposing a method to fuse the results of a) E-mail: duan jianyong@163.com b) E-mail: wuyuwei1996@foxmail.com c) E-mail: wuml@ncut.edu.cn d) E-mail: wanghaomails@gmail.com DOI: 10.1587/transinf.2019EDP7083 multiple models [3]- [5].…”
Section: Introductionmentioning
confidence: 99%