@inproceedings{taniguchi-etal-2018-integrating,
title = "Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification",
author = "Taniguchi, Motoki and
Taniguchi, Tomoki and
Takahashi, Takumi and
Miura, Yasuhide and
Ohkuma, Tomoko",
editor = "Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5520/",
doi = "10.18653/v1/W18-5520",
pages = "124--126",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification
%A Taniguchi, Motoki
%A Taniguchi, Tomoki
%A Takahashi, Takumi
%A Miura, Yasuhide
%A Ohkuma, Tomoko
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F taniguchi-etal-2018-integrating
%X 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.
%R 10.18653/v1/W18-5520
%U https://aclanthology.org/W18-5520/
%U https://doi.org/10.18653/v1/W18-5520
%P 124-126
Markdown (Informal)
[Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification](https://aclanthology.org/W18-5520/) (Taniguchi et al., EMNLP 2018)
ACL