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Chen, L.; Fang, H.: ¬An automatic method for ex-tracting innovative ideas based on the Scopus® database (2019)
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- Abstract
- The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stopwords and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.
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He, Q.: Knowledge discovery through co-word analysis (1999)
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Ahonen, H.: Knowledge discovery in documents by extracting frequent word sequences (1999)
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He, Q.: ¬A study of the strength indexes in co-word analysis (2000)
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- Abstract
- Co-word analysis is a technique for detecting the knowledge structure of scientific literature and mapping the dynamics in a research field. It is used to count the co-occurrences of term pairs, compute the strength between term pairs, and map the research field by inserting terms and their linkages into a graphical structure according to the strength values. In previous co-word studies, there are two indexes used to measure the strength between term pairs in order to identify the major areas in a research field - the inclusion index (I) and the equivalence index (E). This study will conduct two co-word analysis experiments using the two indexes, respectively, and compare the results from the two experiments. The results show, due to the difference in their computation, index I is more likely to identify general subject areas in a research field while index E is more likely to identify subject areas at more specific levels
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Radev, D.R.; Joseph, M.T.; Gibson, B.; Muthukrishnan, P.: ¬A bibliometric and network analysis of the field of computational linguistics (2016)
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Moohebat, M.; Raj, R.G.; Kareem, S.B.A.; Thorleuchter, D.: Identifying ISI-indexed articles by their lexical usage : a text analysis approach (2015)
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Levin, M.; Krawczyk, S.; Bethard, S.; Jurafsky, D.: Citation-based bootstrapping for large-scale author disambiguation (2012)
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