ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 159–163, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing Wenjie Liu, Chengjie Sun, Lei Lin and Bingquan Liu School of Computer Science and Technology Harbin Institute of Technology Harbin, China {wjliu, cjsun, linl, liubq}@insun.hit.edu.cn Abstract Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLP- AiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English monolingual pairs. In our system, rich features are involved, including On- tology based, word embedding based, Cor- pus based, Alignment based and Literal based feature. We leveraged the features to predict sentence pair similarity by a Sup- port Vector Regression (SVR) model. In the result, a Pearson Correlation of 0.8231 is achieved by our system, which is a com- petitive result in the contest of this track. 1 Introduction Semantic Evaluation (SemEval) contest devotes to pushing the research of semantic analysis, which attracts many participants and promote a lot of groundbreaking achievements in natural language processing (NLP) field. Semantic textual simi- larity (STS) task works for computing word and text semantics, which has made extensive attrac- tion to the researchers and NLP community since SemEval 2012 (Agirre et al., 2012). In STS 2017, The organizers added monolin- gual Arabic and Cross-lingual Arabic-English se- mantic calculation in order to increase the diffi- culty in the contest. The task definition is given two sentences participating systems are asked to predict a continuous similarity score on a scale from 0 to 5 of the sentence pair, with 0 indicating that the semantics of the sentences completely in- dependent and 5 semantic equivalence. The eval- uation criterion uses Pearson Correlation Coeffi- cient, which computing the correlation between golden standard scores and semantic system pre- dicted scores. In our system, in order to predict similarity score of two sentences, we trained a Support Vec- tor Regression (SVR) model with abundant fea- tures including Ontology based features, Word Embedding based features, Corpus based features, Alignment based features and Literal based fea- tures. All the English training, trial and evalua- tion data set released by previous STS tasks in Se- mEval were used to construct our system. Our best system achieved 0.8231 Pearson Correlation coef- ficient in the SemEval 2017 evaluation data set, and the winner achieved 0.8547. 2 Feature Engineering In our system, many features are tried to promote the performance of our system. Five kinds of fea- tures are used: Ontology based features, Word Embedding based features, Corpus based features, Alignment based features and Literal based fea- tures.The following is a detailed description of the five kinds features. 2.1 Ontology Based Features WordNet (Miller, 1995) is used to exploit On- tology based features. WordNet is a large lexical database of English. In WordNet, nouns, verbs, adjectives and adverbs are di- vided into …

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