Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings |
| Seiten | 1990-1999 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9781945626838 |
| Publikationsstatus | Veröffentlicht - Sept. 2017 |
| Veranstaltung | 2017 Conference on Empirical Methods in Natural Language Processing - Copenhagen, Dänemark Dauer: 7 Sept. 2017 → 11 Sept. 2017 |
Abstract
Verifiability is one of the core editing principles in Wikipedia, editors being encouraged to provide citations for the added content. For a Wikipedia article, determining the citation span of a citation, i.e. what content is covered by a citation, is important as it helps decide for which content citations are still missing. We are the first to address the problem of determining the citation span in Wikipedia articles. We approach this problem by classifying which textual fragments in an article are covered by a citation. We propose a sequence classification approach where for a paragraph and a citation, we determine the citation span at a fine-grained level. We provide a thorough experimental evaluation and compare our approach against baselines adopted from the scientific domain, where we show improvement for all evaluation metrics.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Theoretische Informatik und Mathematik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings. 2017. S. 1990-1999.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Fine-Grained Citation Span Detection for References in Wikipedia
AU - Fetahu, Besnik
AU - Markert, Katja
AU - Anand, Avishek
N1 - Funding information: This work is funded by the ERC Advanced Grant ALEXANDRIA (grant no. 339233), and H2020 AFEL project (grant no. 687916).
PY - 2017/9
Y1 - 2017/9
N2 - Verifiability is one of the core editing principles in Wikipedia, editors being encouraged to provide citations for the added content. For a Wikipedia article, determining the citation span of a citation, i.e. what content is covered by a citation, is important as it helps decide for which content citations are still missing. We are the first to address the problem of determining the citation span in Wikipedia articles. We approach this problem by classifying which textual fragments in an article are covered by a citation. We propose a sequence classification approach where for a paragraph and a citation, we determine the citation span at a fine-grained level. We provide a thorough experimental evaluation and compare our approach against baselines adopted from the scientific domain, where we show improvement for all evaluation metrics.
AB - Verifiability is one of the core editing principles in Wikipedia, editors being encouraged to provide citations for the added content. For a Wikipedia article, determining the citation span of a citation, i.e. what content is covered by a citation, is important as it helps decide for which content citations are still missing. We are the first to address the problem of determining the citation span in Wikipedia articles. We approach this problem by classifying which textual fragments in an article are covered by a citation. We propose a sequence classification approach where for a paragraph and a citation, we determine the citation span at a fine-grained level. We provide a thorough experimental evaluation and compare our approach against baselines adopted from the scientific domain, where we show improvement for all evaluation metrics.
UR - http://www.scopus.com/inward/record.url?scp=85066893853&partnerID=8YFLogxK
U2 - 10.18653/v1/D17-1
DO - 10.18653/v1/D17-1
M3 - Conference contribution
AN - SCOPUS:85066893853
SP - 1990
EP - 1999
BT - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
T2 - 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Y2 - 7 September 2017 through 11 September 2017
ER -