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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
In the age of Internet, with the online information explosive growth, people want to find information we need in the cyberworld fleetly and exactly. The information retrieval method based on the keyword or the simple logic-combination of the keywords has been unable to meet the people's need of information getting to
profiles five kinds of academic resources from four features including resource type, disciplinary distribution, keyword distribution and LDA topic distribution. After fusing user behaviors and resource profiles, the users' preferences are modeled. Finally, the top-N recommendation is made according to user's interest value
analyze, evaluate and apply Zipf's Law in Computer Science through the content analysis of literature published in ACM journals. The study is focused on the analysis of 13, 053 unique keywords out of 107,467 total keywords retrieved from 1954 to 2008 from Journals. Further, a total of 748 keywords have been chosen for this
Expert finding would be useful for a recommendation system which people want to find experts in a specific area. This paper proposes a model to identify focus research areas of computer science researchers thru their publications. We collect title, abstract and keywords of academic papers published in IEEE Xplore
This article describes an algorithm to facilitate the proper assignment of reviewers by finding an author's profile. It uses an original approach to analyzing publications published in digital libraries to get additional keywords based on NLP (natural language processing) techniques. Comparing profiles and finding
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