In this paper, we describe the TATO system which participated in the SemEval-2015 Task 2a: “Semantic Textual Similarity (STS) for English”. Our system is trained on published datasets from the previous competitions. Based on some machine learning techniques, it combines multiple similarity measures of varying complexity ranging from simple lexical and syntactic similarity measures to complex se...

The paper aims to come up with a system that examines the degree of semantic equivalence between two sentences. At the core of the paper is the attempt to grade the similarity of two sentences by finding the maximal weighted bipartite match between the tokens of the two sentences. The tokens include single words, or multiwords in case of Named Entitites, adjectivally and numerically modified wo...

We present an algorithm for computing the semantic similarity between two sentences. It adopts the hypothesis that semantic similarity is a monotonically increasing function of the degree to which (1) the two sentences contain similar semantic units, and (2) such units occur in similar semantic contexts. With a simplistic operationalization of the notion of semantic units with individual words,...

Defining similarity measures is a crucial task when developing CBR applications. Particularly, when employing utility-based similarity measures rather than pure distance-based measures one is confronted with a difficult knowledge engineering task. In this paper we point out some problems of the state-of-the-art procedure to defining similarity measures. To overcome these problems we propose an ...

In many data mining applications, both classification and clustering algorithms require a distance/similarity measure. The central problem in similarity based clustering/classification comprising sequential data is deciding an appropriate similarity metric. The existing metrics like Euclidean, Jaccard, Cosine, and so forth do not exploit the sequential nature of data explicitly. In this chapter...

We present our approach to measuring semantic similarity of sentence pairs used in Semeval 2015 tasks 1 and 2. We adopt the sentence alignment framework of (Han et al., 2013) and experiment with several measures of word similarity. We hybridize the common vector-based models with definition graphs from the 4lang concept dictionary and devise a measure of graph similarity that yields good result...

Sentences that are syntactically quite different can often have similar or same meaning. The SemEval 2012 task of Semantic Textual Similarity aims at finding the semantic similarity between two sentences. The semantic representation of Universal Networking Language (UNL), represents only the inherent meaning in a sentence without any syntactic details. Thus, comparing the UNL graphs of two sent...

This study examines the perception of melodic similarity applied to cases of melodic plagiarism under a review of similarity measures. An implicit memory task was designed to test the extent of the participants’ confusability of two similar melodies. The participants were able to distinguish between such melodies involved in cases with and without actual copyright infringement. Many of the appl...

VRep is a system designed for SemEval 2016 Task 1 Semantic Textual Similarity (STS) and Task 2 Interpretable Semantic Textual Similarity (iSTS). STS quantifies the semantic equivalence between two snippets of text, and iSTS provides a reason why those snippets of text are similar. VRep makes extensive use of WordNet for both STS, where the Vector relatedness measure is used, and for iSTS, where...

In this paper we describe our system (DTSim) submitted at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity (iSTS). We participated in both gold chunks category (texts chunked by human experts and provided by the task organizers) and system chunks category (participants had to automatically chunk the input texts). We developed a Conditional Random Fields based chunker and applied r...