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The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 6, p e0199303 (2018)
- Publication Year :
- 2018
- Publisher :
- Public Library of Science, 2018.
-
Abstract
- The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing.
- Subjects :
- Semantic link
Computer and Information Sciences
Lexical semantics
Computer science
Science
lcsh:Medicine
Social Sciences
02 engineering and technology
Semantic data model
Research and Analysis Methods
Systems Science
Automation
Sociology
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Question answering
Psychology
Syntax
lcsh:Science
Data Curation
Language
Grammar
Multidisciplinary
Information retrieval
Applied Mathematics
Simulation and Modeling
Research
lcsh:R
Publications
Cognitive Psychology
Biology and Life Sciences
Linguistics
Complex Systems
Reasoning
Automatic summarization
Semantics
Lexical Semantics
Social Networks
Physical Sciences
Cognitive Science
lcsh:Q
020201 artificial intelligence & image processing
Mathematics
Algorithms
Network Analysis
Research Article
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....ceda99b252f21fc9b9992173eee8f303