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Scientific documents are unstructured data consisting of natural language and hard for scientists to read and manage. Keywords are very helpful for scientists to search the related documents and know about their contents in a prompt way. In this paper we investigate a kind of data preprocessing technique used in SVM
Keyword (Feature) selection enhances and improves many Information Retrieval (IR) tasks such as document categorization, automatic topic discovery, etc. The problem of keyword selection is usually solved using supervised algorithms. In this paper, we propose an unsupervised approach that combines keyword selection and
citation recommendation suffers with the following three limitations. First, most of the existing approaches for citation recommendation require input in the form of either the full article or a seed set of citations, or both. Nevertheless, obtaining the recommendation for citations given a set of keywords is extremely useful
Nowadays, we have to deal with a large quantity of unstructured, heterogeneous data, produced by an increasing number of sources. Clustering heterogeneous data is essential to getting structured information in response to user queries. In this paper, we assess the results of a new clustering technique - clustering by compression - when applied to metadata associated with heterogeneous sets of data...
Recently, the multi-label learning has drawn considerable attention as it has many applications in text classification, image annotation and query/keyword suggestions etc. In recent years, a number of remedies have been proposed to address this challenging task. However, they are either tree based methods which has
propose a hybrid approach where the formal concept analysis is used for finding author's profiles based on keywords and fuzzy rules to learn the properties of the authors and to enhance the set of experts.
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