What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature
| Autor: | Ahmed AbuRa’ed, Horacio Saggion, Luis Chiruzzo |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Value (ethics)
Computer science business.industry Context (language use) 02 engineering and technology Scientific literature computer.software_genre Automatic summarization Information overload 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Tractament del llenguatge natural (Informàtica) Citation business GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) computer Sentence Natural language processing |
| Zdroj: | RANLP Recercat. Dipósit de la Recerca de Catalunya instname |
| ISSN: | 2603-2813 |
| DOI: | 10.26615/978-954-452-049-6_002 |
| Popis: | Comunicació presentada a la International Conference Recent Advances in Natural Language Processing (RANLP 2017), celebrada els dies 2 a 8 de setembre de 2017 a Varna, Bulgària. In the current context of scientific information overload, text mining tools are of paramount importance for researchers who have to read scientific papers and assess their value. Current citation networks, which link papers by citation relationships (reference and citing paper), are useful to quantitatively understand the value of a piece of scientific work, however they are limited in that they do not provide information about what specific part of the reference paper the citing paper is referring to. This qualitative information is very important, for example, in the context of current community-based scientific summarization activities. In this paper, and relying on an annotated dataset of co-citation sentences, we carry out a number of experiments aimed at, given a citation sentence, automatically identify a part of a reference paper being cited. Additionally our algorithm predicts the specific reason why such reference sentence has been cited out of five possible reasons. This work is (partly) supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE). |
| Databáze: | OpenAIRE |
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