Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in iso...

Current longitudinal image registration methods rely on the assumption that image appearance between time-points remains constant or changes uniformly within intensity classes. This assumption, however, is not valid for magnetic resonance imaging of brain development. Registration methods developed to align images with non-uniform appearance change either (i) locally minimize some global simila...

Given a large collection of objects, finding all pairs of similar objects, namely similarity join, is widely used to solve various problems in many application domains.Computation time of similarity join is critical issue, since similarity join requires computing similarity values for all possible pairs of objects. Several existing algorithms adopt prefix filtering to avoid unnecessary similari...

Similarity method is the key of the user-based collaborative filtering recommend algorithm. The traditional similarity measures, which cosine similarity, adjusted cosine similarity and Pearson correlation similarity are included, have some advantages such as simple, easy and fast, but with the sparse dataset they may lead to bad recommendation quality. In this article, we first research how the...

The 2017 Microblog Cultural Contextualization task consists in three challenges: (1) Content Analysis, (2) Microblog search, and (3) TimeLine illustration. This paper describes the use of cosine similarity, which is characterized by the comparison of similarity between two vectors of an inner product space. This research used two approaches: (1) word2vec and (2) Bag-of-Words (BoW) for extractin...

Article history: Received 21 September 2009 Received in revised form 23 August 2010 Accepted 23 August 2010 Available online 8 September 2010 Recent years have witnessed an increased interest in computing cosine similarity in many application domains. Most previous studies require the specification of a minimum similarity threshold to perform the cosine similarity computation. However, it is us...

Question answering for Machine reading evaluation track is a aim to check machine understanding ability of a machine.As we analyzed most crusial part for efficient working of this system is to select text which needs to be considered for understanding since understanding text would involve a lot of NLP processing. This paper covers our submitted system for QA4MRE campaign, Which mostly focuses ...

Face veri cation is the task of deciding by analyzing face images, whether a person is who he/she claims to be. This is very challenging due to image variations in lighting, pose, facial expression, and age. The task boils down to computing the distance between two face vectors. As such, appropriate distance metrics are essential for face veri cation accuracy. In this paper we propose a new met...

In this paper, a comparison between Cosine Similarity and k-Nearest Neighbors algorithm in Latent Semantic Analysis method to score Arabic essays automatically is presented. It also improves Latent Semantic Analysis by processing the entered text, unifying the form of letters, deleting the formatting, replacing synonyms, stemming and deleting "Stop Words". The results showed that the use of Cos...

A graph in van Eck and Waltman [JASIST 60(8), 2009, p. 1644], representing the relation between the Association Strength and the Cosine, is partially explained as a sheaf of parabolas, each parabola being the functional relation between these similarity measures on the trajectories . X Y a  , a constant. Based on earlier obtained relations between Cosine and other similarity measures (such as ...