Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification

Motoki Taniguchi, Tomoki Taniguchi, Takumi Takahashi, Yasuhide Miura, Tomoko Ohkuma

Abstract
We describe here our system and results on the FEVER shared task. We prepared a pipeline system which composes of a document selection, a sentence retrieval, and a recognizing textual entailment (RTE) components. A simple entity linking approach with text match is used as the document selection component, this component identifies relevant documents for a given claim by using mentioned entities as clues. The sentence retrieval component selects relevant sentences as candidate evidence from the documents based on TF-IDF. Finally, the RTE component selects evidence sentences by ranking the sentences and classifies the claim simultaneously. The experimental results show that our system achieved the FEVER score of 0.4016 and outperformed the official baseline system.
Anthology ID:
W18-5520
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
124–126
Language:
URL:
https://aclanthology.org/W18-5520
DOI:
10.18653/v1/W18-5520
Bibkey:
Cite (ACL):
Motoki Taniguchi, Tomoki Taniguchi, Takumi Takahashi, Yasuhide Miura, and Tomoko Ohkuma. 2018. Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 124–126, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification (Taniguchi et al., EMNLP 2018)
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https://preview.aclanthology.org/nschneid-patch-3/W18-5520.pdf
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