Coverage of Information Extraction from Sentences and Paragraphs

Simon Razniewski, Nitisha Jain, Paramita Mirza, Gerhard Weikum

Abstract
Scalar implicatures are language features that imply the negation of stronger statements, e.g., “She was married twice” typically implicates that she was not married thrice. In this paper we discuss the importance of scalar implicatures in the context of textual information extraction. We investigate how textual features can be used to predict whether a given text segment mentions all objects standing in a certain relationship with a certain subject. Preliminary results on Wikipedia indicate that this prediction is feasible, and yields informative assessments.
Anthology ID:
D19-1583
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5771–5776
Language:
URL:
https://aclanthology.org/D19-1583
DOI:
10.18653/v1/D19-1583
Bibkey:
Cite (ACL):
Simon Razniewski, Nitisha Jain, Paramita Mirza, and Gerhard Weikum. 2019. Coverage of Information Extraction from Sentences and Paragraphs. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5771–5776, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Coverage of Information Extraction from Sentences and Paragraphs (Razniewski et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/D19-1583.pdf
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