WSL: Sentence Similarity Using Semantic Distance Between Words

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 128–131, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics WSL: Sentence Similarity Using Semantic Distance Between Words Naoko Miura Tomohiro Takagi Meiji University, Japan 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi,Kanagawa 214–8571 E-mail:{n_miura,takagi}@cs.meiji.ac.jp Abstract A typical social networking service contains huge amounts of data, and analyzing this data at the level of the sentence is important. In this paper, we describe our system for a SemEval2015 semantic textual similarity task (task2). We present our approach, which uses edit distance to consider word order, and in- troduce word appearance in context. We re- port the results from SemEval2015. 1 Introduction The Internet, particularly sites related to social networking services (SNS), contains a vast array of information used for a variety of purposes. The vector space model is conventionally used for nat- ural language processing. This model creates vec- tors on the basis of frequency of word appearance and co-occurring words, without taking word order into account. When it comes to short texts, word co-occurrence is rare (or even non-existent), and the number of words is often less than in a typical newspaper article. Because the average SNS con- tains data consisting mostly of short sentences, the vector space model is not the best choice. In this work, we describe a system we developed and submitted to SemEval2015. In the proposed system, we compute sentence similarity using edit distance to consider word order along with the se- mantic distance between words. We also introduce word appearance in context. The rest of this paper is organized as follows. Section 2 reviews related work and in Section 3 we present the three systems we submitted for SemEval2015. In Section 4, we discuss the results of our evaluation at SemEval2015.We conclude in Section 5 with a brief summary. Fig. 1. Hierarchical semantic knowledge base (Li et al., 2006). 2 Related Work Recent research has introduced the lexical database as a dictionary to analyze short texts(Aziz et al.,2010). Aziz uses a set of similar noun phrases and similar verb phrases and common words to compute sentence similarity. Li combines semantic similarity between words into a hierarchical se- mantic knowledge base and word order(Li et al.,2006). There are currently a few hierarchical semantic knowledge bases available, one of which is WordNet(Miller,1995). WordNet contains 155,287 words and 117,659 synsets that were stored in 2012 into the lexical categories of nouns, verbs, adjectives, and adverbs(WordNet Statistics, 2014). All synsets have semantic relation to other synsets. An example in the case of using nouns is shown in Fig.1. Li proposed a formula to measure the similarity s(w1,w2) between words w1 and w2 as h h h h l e e e e e sww           ) , ( 2 1 , (1) 128 where l is the shortest path length between w1 and w2 and h is the depth of the subsumer of w1 and w2 in WordNet. For example, …

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