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| Cognitive Analysis Method for Mining Innovation Points in Academic Abstracts |
| Wen Hao, He Qianru |
| School of Information and Control Engineering, Xi'an University of Architecture Technology, Xi'an 710055 |
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Abstract To resolve difficulties in knowledge mining algorithms due to the diversity and richness of academic abstracts' expression innovation points, this paper proposes a cognitive analysis method for innovation points mining in academic abstracts. The method comprises the following cognitive analyses: academic abstract innovation point report, lexical semantic distribution consistency, predicate verb semantic understanding, pragmatic function classification, and syntactic implicit. The research results show that these five kinds of analyses can form five levels of abstract mining, namely information retrieval, ontology construction, semantic mining, pragmatic classification, and object hidden levels. This cognitive analysis method processes natural language expression patterns using machine learning algorithms and improves the accuracy and coverage of the classification algorithm for abstract innovation points. Furthermore, it improves the efficiency of abstract “question, method, and result” triples mining. Such an intelligent question answering system based on the triad knowledge base improves the guiding roles of theory and method.
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Received: 13 March 2020
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