Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 51–55, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgram Taneeya Satyapanich, Hang Gao and Tim Finin University of Maryland, Baltimore County Baltimore, MD, 21250, USA taneeya1@umbc.edu, hanggao1@umbc.edu, finin@umbc.edu Abstract We describe the system we developed to partic- ipate in SemEval 2015 Task 1, Paraphrase and Semantic Similarity in Twitter. We create simi- larity vectors from two-skip trigrams of prepro- cessed tweets and measure their semantic simi- larity using our UMBC-STS system. We sub- mit two runs. The best result is ranked eleventh out of eighteen teams with F1 score of 0.599. 1. Introduction In this task (Wei, et al., 2015), participants were given pairs of text sequences from Twitter trends and produced a binary judgment for each stating whether or not they are paraphrases (e.g., semanti- cally the same) and optionally a graded score (0.0 to 1.0) measuring their degree of semantic equiva- lence. For example, for the trending topic “A Walk to Remember” (a film released in 2002), the pair A Walk to Remember is the definition of true love” and “A Walk to Remember is on and Im in town and Im upset” might be judged as not paraphrases with score 0.2 whereas the pair “A Walk to Re- member is the definition of true love” and “A Walk to Remember is the cutest thing” could be judged as paraphrases with a score of 0.6. Many methods have been proposed to solve the paraphrase detection problem. Early approaches were often based on lexical matching techniques, e.g., word n-gram overlap (Barzilay and Lee, 2003) or predicate argument tuple matching (Qiu, et al., 2006). Some other approaches that go be- yond simple lexical matching have also been de- veloped. For example, (Mihalcea, et al., 2006) es- timated semantic similarity of sentence pairs with word-to-word similarity measures and a word specificity measure. (Zhang and Patrick, 2005) uses text canonicalization to transfer texts of simi- lar meaning into the same surface text with a high- er probability than those with different meaning. Many of these approaches adopt distributional semantic models, but limited to a word level. To extend distributional semantic models beyond words, several researchers have learned phrase or sentence representation by composing the repre- sentation of individual words (Mitchell and Lapata, 2010; Baroni and Zamparelli, 2010). An alternative approach by (Socher et al., 2011) represents phrases and sentences with fixed matrices consist- ing of pooled word and phrase pairwise similari- ties. (Le and Mikolov, 2014) learns representation of sentences directly by predicting context without composition of words. In our work, we judge that two sentences are paraphrases if they have high degree of semantic similarity. We use the UMBC-Semantic Textual Similarity system (Lushan Han et al., 2013), which provides high accurate semantic similarity meas- urement. The remainder of this paper is organized as follows. Section 2 describes the task and the details of our method. Section 3 presents …

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