in many signal processing applications, an appropriate measure to comparetwo signals plays a fundamental role in both implementing the algorithm andevaluating its performance. several techniques have been introduced in literature assimilarity measures. however, the existing measures are often either impractical forsome applications or they have unsatisfactory results in some other applications....

Plenty of researches have been carried out, focusing on the measures of distance, similarity, and correlation between intuitionistic fuzzy sets (IFSs).However, most of them are single-valued measures and lack of potential for efficiency validation.In this paper, a new vector valued similarity measure for IFSs is proposed based on OWA operators.The vector is defined as a two-tuple consisting of ...

This paper describes our system submission to the SemEval 2016 English Semantic Textual Similarity (STS) shared task. The proposed system is based on the compositional text similarity model, which aggregates pairwise word similarities for computing the semantic similarity between texts. In addition, our system combines word importance and word similarity to build an importance-similarity matrix...

Motivated by evidence in psycholinguistics and cognition, we propose a hierarchical distributed semantic model (DSM) that consists of low-dimensional manifolds built on semantic neighborhoods. Each semantic neighborhood is sparsely encoded and mapped into a low-dimensional space. Global operations are decomposed into local operations in multiple sub-spaces; results from these local operations a...

Usage-based theories of syntax predict that words and syntactic constructions are probabilistically interconnected. If this is true, then words that occur in similar distributions of syntactic constructions should prime each other. These effects should be fine-grained; even small differences between the syntactic distributions of pairs of words of the same grammatical category should cause vari...

We propose a new similarity measure between texts which, contrary to the current state-of-the-art approaches, takes a global view of the texts to be compared. We have implemented a tool to compute our textual distance and conducted experiments on several corpuses of texts. The experiments show that our methods can reliably identify different global types of texts.