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Ranking solutions is an important issue in Information Retrieval because it greatly influences the quality of results. In this context, keyword based search approaches use to consider solutions sorting as least step of the overall process. Ranking and building solutions are completely separate steps running
user to get specific information related to the submitted keyword. For this reason a new criterion is used in which feedback sessions are first generated from user clicked through logs. Using Feedback session a pseudo documents are generated by calculating TF-IDF (Term Frequency Inverse Data Frequency) vectors for each
Document clustering is to group documents according to a certain semantic features defined on the document set for measuring the similarities between two documents. The keyword models such as the TFIDF model of document have been widely used as features for document clustering. But it lacks of semantic structure
Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that
documents' keywords as nodes and the colocation of those keywords in a document as edges. We then exploit the particular nature of such graphs where co referent words are topologically clustered and can be efficiently discovered by our community detection algorithm. The accuracy of our technique is considerably higher than
vocabulary. A group-LASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experiments on PASCAL VOC 2007 dataset using 5000 visual keywords, resulted in around 80% reduction in the number of
Traditional hierarchical text clustering methods assume that the documents are represented only by "technical information", i.e., keywords, phrases, expressions and named entities that can be directly extracted from the texts. However, in many scenarios there is an additional and valuable information
Unlike traditional multimedia content, content generated on social media platforms such as YouTube, Flickr etc are usually annotated with rich set of social tags such as keywords, textual description, category information, author's profile etc. In this paper we investigate the use of such social tag information for
redundancy. Then, in order to group them according to topics, those sentences are clustered considering the collection of keywords. Finally, the summarization process includes a sentence simplification step, which aims not only to create simpler and more incisive sentences, but also to make room for the inclusion of further
learning objects are stored in repositories and are managed by Learning Management Systems. However, the exponential availability of information leads to a difficult scenario like finding a particular educational resource for a learner, based on the context or based on his/her preferences. The searching through keywords or
reflected by the changes in both the query keywords and news/blogs content during the event happening period. We propose a two-stage real-time event detection framework consisting of event fragment detection and event detection. The proposed framework integrates queries, news articles, and blog posts through the notion of
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