This paper explores methods to alleviate the effect of lexical sparseness in the classification of verbal arguments. We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classification. The best results are obtained with a novel second-order distributional similarity measure, and the posi...

This paper proposes a distance measure of neutrosophic numbers and a similarity measure based on cosine function, and then develops the misfire fault diagnosis method of gasoline engines by using the cosine similarity measure of neutrosophic numbers. In the fault diagnosis, by the cosine similarity measure between the fault knowledge (fault patterns) and required diagnosistesting sample with ne...

Using a large unique patent-strategic alliance dataset over the period 1990 to 2004, we first show that firms faced with greater technological competition are more likely to form alliances. Technological competition is captured by a cosine similarity measure between a firm’s own patent output and the patent output of all other firms in the economy. We further show that alliances lead to more pa...

Clustering is one of the most interesting and important tool for research in data mining and other disciplines. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the appropriateness of the similarity measure between the data in which the similarity can be computed. This pap...

Here we describe our algorithm for detecting external plagiarism in PAN-10 competition. The algorithm has two steps 1. Identification of similar documents and the plagiarized section for a suspicious document with the source documents using Vector Space Model (VSM) and cosine similarity measure and 2. Identify the plagiarized area in the suspicious document using Chunk ratio.

Nearest-neighbor collaborative filtering (CF) algorithms are gaining widespread acceptance in recommender systems and e-commerce applications. These algorithms provide recommendations for products, based on suggestions of users with similar preferences. One of the most crucial factors in the effectiveness of nearest-neighbor CF algorithms is the similarity measure that is used. The most popular...

Abstract Focused Crawler aims to select relevant web pages from internet. These pages are relevant to some predefined topics. Previous focused crawlers have a problem of not keeping track of user interest and goals .The topic weight table is calculated only once statically and that is less sensitive to potential changes in environment. To address this problem we design a focused crawler based o...

Classification is a common task in Machine Learning and Data Mining. Jumping Emerging Patterns have been applied for classification in different contexts with good results and the advantage of to be easily understandable for users. In this work we propose the use of cosine similarity measure to select the patterns which will be used to predict the classes in the classification process. Two vers...

Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Ba...

In this paper, we define a rough cosine similarity measure between two rough neutrosophic sets. The notions of rough neutrosophic sets (RNS) will be used as vector representations in 3D-vector space. The rating of all elements in RNS is expressed with the upper and lower approximation operator and the pair of neutrosophic sets which are characterized by truth-membership degree, indeterminacy-me...