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Identifying the Conceptual Space of Citation Contexts using Coreferences
- Source :
- BIRNDL 2019 Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital LibrariesProceedings of the 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) at the 42ndInternational ACM SIGIR Conference on Research and Development in Information Retrieval, 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) at the 42ndInternational ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, Jul 2019, Paris, France. pp.138-144, HAL
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
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Abstract
- International audience; The study of citation contexts is an important element in understanding the function of citations and categorizing the relationshipsbetween works. One of the problems in this field is defining the size of citation contexts. In this paper we propose the definition of citationblocks (CB) that are citation contexts composed of one or more sentences that are linked by coreference clusters. We describe the methodology forthe automatic processing and determining the boundaries of CB and observe the different sizes of CB in the different sections of the IMRaDstructure of articles. The results are obtained from a sample of 70,000 citation contexts extracted from the PLOS dataset.
- Subjects :
- [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
Coreference Resolution
[INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL]
[SHS.INFO]Humanities and Social Sciences/Library and information sciences
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing
[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
Citation contexts
Deep learning
[SHS.INFO] Humanities and Social Sciences/Library and information sciences
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- BIRNDL 2019 Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital LibrariesProceedings of the 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) at the 42ndInternational ACM SIGIR Conference on Research and Development in Information Retrieval, 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) at the 42ndInternational ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, Jul 2019, Paris, France. pp.138-144, HAL
- Accession number :
- edsair.dedup.wf.001..40029345554a5c62571a96b9358b71cf