> Detailanzeige

D'Souza, Jennifer [Verfasser:in]; Auer, Sören [Verfasser:in]; Pedersen, Ted [Verfasser:in]; Palmer, Alexis [Verfasser:in]; Schneider, Nathan [Verfasser:in]; Schluter, Natalie [Verfasser:in]; Emerson, Guy [Verfasser:in]; Herbelot, Aurelie [Verfasser:in]; Zhu, Xiaodan [Verfasser:in]

SemEval-2021 Task 11: NLPCONTRIBUTIONGRAPH - Structuring Scholarly NLP Contributions for a Research Knowledge Graph - [published Version]

Literatur-
verwaltung

Zur
Merkliste

Lösche von
Merkliste

Per Whatsapp teilen

Schließen

Merkliste



Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
  • Medientyp: E-Artikel; Sonstige Veröffentlichung
  • Titel: SemEval-2021 Task 11: NLPCONTRIBUTIONGRAPH - Structuring Scholarly NLP Contributions for a Research Knowledge Graph
  • Beteiligte: D'Souza, Jennifer [Verfasser:in]; Auer, Sören [Verfasser:in]; Pedersen, Ted [Verfasser:in]; Palmer, Alexis [Verfasser:in]; Schneider, Nathan [Verfasser:in]; Schluter, Natalie [Verfasser:in]; Emerson, Guy [Verfasser:in]; Herbelot, Aurelie [Verfasser:in]; Zhu, Xiaodan [Verfasser:in]
  • Erschienen: Stroudsburg, PA : Association for Computational Linguistics (ACL), 2021
  • Erschienen in: The 15th International Workshop on Semantic Evaluation (SemEval-2021) - proceedings of the workshop
  • Ausgabe: published Version
  • Sprache: Englisch
  • DOI: https://doi.org/10.15488/16286; https://doi.org/10.18653/v1/2021.semeval-1.44
  • ISBN: 978-1-954085-70-1
  • Schlagwörter: Konferenzschrift ; Semantics ; Automation ; Natural language processing systems ; Computational linguistics ; Modeling languages
  • Entstehung:
  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. The SemEval-2021 Shared Task NLPContributionGraph (a.k.a. ‘the NCG task’) tasks participants to develop automated systems that structure contributions from NLP scholarly articles in the English language. Being the first-of-its-kind in the SemEval series, the task released structured data from NLP scholarly articles at three levels of information granularity, i.e. at sentence-level, phrase-level, and phrases organized as triples toward Knowledge Graph (KG) building. The sentence-level annotations comprised the few sentences about the article’s contribution. The phrase-level annotations were scientific term and predicate phrases from the contribution sentences. Finally, the triples constituted the research overview KG. For the Shared Task, participating systems were then expected to automatically classify contribution sentences, extract scientific terms and relations from the sentences, and organize them as KG triples. Overall, the task drew a strong participation demographic of seven teams and 27 participants. The best end-to-end task system classified contribution sentences at 57.27% F1, phrases at 46.41% F1, and triples at 22.28% F1. While the absolute performance to generate triples remains low, as conclusion to the article, the difficulty of producing such data and as a consequence of modeling it is highlighted.
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung (CC BY)