This paper describes a new approach to document classification based on visual features alone. Text-based retrieval systems perform poorly on noisy text. We have conducted series of experiments using cosine distance as our similarity measure, selecting varying numbers local interest points per page, and varying numbers of nearest neighbour points in the similarity calculations. We have found th...

In this paper we present an approach to person name disambiguation that clusters documents on the basis of textual features using cosine similarity and a machinely learned meta similarity measure. The approach achieves an F-measure of B-Cubed Precision and Recall of 0.74 on the Clustering Subtask for WePS-2. Such task consists of clustering a set of documents that mention an ambiguous person na...

BACKGROUND Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy and coverage. MATERIALS AND METHODS In this paper, a novel method is presented that automatically learns patterns of variations of clinical terms...

In this article we present three similarity measures between simpli…ed neutrosophic hesitant fuzzy sets, which contain the concept of single valued neutrosophic hesitant fuzzy sets and interval valued neutrosophic hesitant fuzzy sets, based on the extension of Jaccard similarity measure, Dice similarity measure and Cosine similarity in the vector space. Then based on these three de…ned similari...

Ordered sets of documents are encountered more and more in information distribution systems, such as information retrieval systems (IRS). Classical similarity measures for ordinary sets of documents hence need to be extended to these ordered sets. This is done in this paper using fuzzy set techniques. First a general similarity measure is developed which contains the classical strong similarity...

Many machine learning and data mining algorithms on the similarity metrics. The Cosine similarity, wh the inner product of two normalized feature vectors, most commonly used similarity measures. Howev practical tasks such as text categorization an clustering, the Cosine similarity is calculated assumption that the input space is an orthogonal usually could not be satisfied due to synonymy an Va...

In this paper, we present a comparison of collocation-based similarity measures: Jaccard, Dice and Cosine similarity measures for the proper selection of additional search terms in query expansion. In addition, we consider two more similarity measures: average conditional probability (ACP) and normalized mutual information (NMI). ACP is the mean value of two conditional probabilities between a ...

The ACORN provides an agent based architecture for infor mation retrieval and provision across networks The main objective of this paper is to present the design and implementation of information sharing in a community of mobile agents in ACORN based on keyphrase sharing and comparison among agents In keyphrase based information sharing keyphrases and their weights are used to represent user in...

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...