NLP-driven citation analysis for scientometrics
| Autor: | Dragomir R. Radev, Amjad Abu Jbara, Vahed Qazvinian, Rahul Jha |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Linguistics and Language
Computer science business.industry 05 social sciences Sentiment analysis Bibliometrics Scientometrics 050905 science studies computer.software_genre Automatic summarization Language and Linguistics Artificial Intelligence Citation analysis Artificial intelligence 0509 other social sciences 050904 information & library sciences Citation business GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) computer Software Natural language processing Sentence Scope (computer science) |
| Zdroj: | Natural Language Engineering. 23:93-130 |
| ISSN: | 1469-8110 1351-3249 |
| DOI: | 10.1017/s1351324915000443 |
| Popis: | This paper summarizes ongoing research in Natural-Language-Processing-driven citation analysis and describes experiments and motivating examples of how this work can be used to enhance traditional scientometrics analysis that is based on simply treating citations as a ‘vote’ from the citing paper to cited paper. In particular, we describe our dataset for citation polarity and citation purpose, present experimental results on the automatic detection of these indicators, and demonstrate the use of such annotations for studying research dynamics and scientific summarization. We also look at two complementary problems that show up in Natural-Language-Processing-driven citation analysis for a specific target paper. The first problem is extracting citation context, the implicit citation sentences that do not contain explicit anchors to the target paper. The second problem is extracting reference scope, the target relevant segment of a complicated citing sentence that cites multiple papers. We show how these tasks can be helpful in improving sentiment analysis and citation-based summarization. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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