Cited by 527Topic ModelingInformation Retrieval and Search BehaviorNatural Language Processing Techniques

The Web as a Knowledge-Base for Answering Complex Questions

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Talmor, Alon, Berant, Jonathan·2018·Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

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Alon Talmor, Jonathan Berant. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

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  • In ComplexWebQuestions, the proportion of implicit relations is also small.

    “As for other KBQA datasets, e.g., ComplexWebQuestions (Talmor and Berant, 2018), the proportion of implicit relations is also small.”

    From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Talmor and Berant, 2018

    Section 4.2 describes the proportions of question types in ComplexWebQuestions, but does not report a proportion of implicit relations. Searches for “implicit relations” and related terms found no passage in our copy that addresses that proportion; the reported percentages for word reordering concern a different measure. Page numbers were not available in our copy. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Talmor, A., & Berant, J.
Affiliations
Tel Aviv University (IL)
Venue
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Published
2018
Pages
641-651
OpenAlex
W2793065281
Cited by
520
References
0
Citations by year
2026: 75 · 2025: 60 · 2024: 42 · 2023: 74 · 2022: 75 · 2021: 78 · 2020: 59 · 2019: 47 · 2018: 10
FWCI
19.72
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Israel Science Foundation
  • Samsung
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
complex question answering, question answering, knowledge base construction

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