Kausazko koherentzia-erlazioen azterketa automatikoa euskarazko laburpen zientifikoetan

  1. Mikel Iruskieta 1
  2. Maria Jesus Aranzabe 1
  3. Arantza Diaz de Ilarraza 1
  4. Mikel Lersundi 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Aldizkaria:
Gogoa: Euskal Herriko Unibersitateko hizkuntza, ezagutza, komunikazio eta ekintzari buruzko aldizkaria

ISSN: 1577-9424

Argitalpen urtea: 2016

Zenbakien izenburua: Xabier Arrazola Gogoan (1962-2015)

Zenbakia: 14

Orrialdeak: 45-77

Mota: Artikulua

DOI: 10.1387/GOGOA.15628 DIALNET GOOGLE SCHOLAR lock_openSarbide irekia editor

Beste argitalpen batzuk: Gogoa: Euskal Herriko Unibersitateko hizkuntza, ezagutza, komunikazio eta ekintzari buruzko aldizkaria

Garapen Iraunkorreko Helburuak

Laburpena

Detecting automatically the cause relations of a text may be useful in question answering tasks and event information extraction. The aim of this paper is to study how to detect coherence relations of the cause subgroup (CAUSE, RESULT and PURPOSE). TO achieve this aim we have used the Rhetorical Structure Theory (RST) and some automatic linguistic information from different tools developed by IXA Group. We have used a corpus of 60 scientific abstracts, the Basque RST Treebank (Iruskieta et al., 2013), of different domains: science, medicine and terminology. A linguist has annotated all the signals of that corpus and described the most important problems in such task. To report the reliability of this annotator, two linguists have annotated the signals of the cause subgroup and all the annotations were compared and evaluated. After that, a superannotator has harmonized all the signals of those cause relations. Finally, we show the most important signals for such relations.

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