Abstract
With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
Highlights
Due to the huge volume of information on the internet, data are the most valuable thing
We propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding
The paper is organized as follows: Section 2 describes the related works about natural language processing, information retrieval and association rules in the last few years; Section 3 is about the proposed mining tool and some examples of association rules used in this current
Summary
Due to the huge volume of information on the internet, data are the most valuable thing Natural language processing (NLP) plays an important role in developing an automatic text summarization. Scientists and Researchers recognize the ranking of international journals Organizations such as Web of Science, Scopus, DBLP, IEEE and others came to make this classification. The paper is organized as follows: Section 2 describes the related works about natural language processing, information retrieval and association rules in the last few years; Section 3 is about the proposed mining tool and some examples of association rules used in this current.
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