Making use of latent semantic analysis, we explore the hypothesis that local linguistic context can serve to identify multi-word expressions that have noncompositional meanings. We propose that vector-similarity between distribution vectors associated with an MWE as a whole and those associated with its constitutent parts can serve as a good measure of the degree to which the MWE is composition...

The problem of Recommender systems is to apply knowledge discovery techniques and to provide personalized recommendations. A recommender system tracks past actions of a group of users to make recommendations to individual members of the group. In today’s e-commerce world these systems are achieving widespread success. Content based and collaborative filtering based approaches are the two major ...

Understanding questions and answers in Question Answering (QA) system is a major challenge in the domain of natural language processing. In this paper, we present a question answering system that influences the human opinions in a conversation. The opinion words are quantified by using a lexicon-based method. We apply Latent Semantic Analysis and the cosine similarity measure between candidate ...

This paper explores topic aspect (i.e., subtopic or facet) classification for collections that contain more than one language (in this case, English and Chinese), and investigates several key technical issues that may affect the classification effectiveness. The evaluation model assumes a bilingual user who has found some documents on a topic and identified a few passages in each language on sp...

The vector space model is one of the classical and widely applied information retrieval models to rank the web page based on similarity values. The retrieval operations consist of cosine similarity function to compute the similarity values between a given query and the set of documents retrieved and then rank the documents according to the relevance. In this paper, we are presenting different a...

The goal of the paper is to present a novel Chi-square similarity measure and assess its performance through comparison with well-known similarity measures such as Cosine, Dice, and Jaccard. The Chi-square similarity measure has been designed to withstand the imperfections of transcribed spoken documents. The major difference of our similarity measure from others consists in the fact that in ad...

Proximity rank join is the problem of finding the top-K combinations with the highest aggregate score in which the best combinations of objects coming from different services are sought, and each object is equipped with both a score and a real-valued feature vector. The proximity of the objects i.e. the geometry of the feature space plays a distinctive role in the computation of the overall sco...