@inproceedings{kannan-ravi-etal-2021-cholan,
title = "{CHOLAN}: A Modular Approach for Neural Entity Linking on {W}ikipedia and {W}ikidata",
author = "Kannan Ravi, Manoj Prabhakar and
Singh, Kuldeep and
Mulang{'}, Isaiah Onando and
Shekarpour, Saeedeh and
Hoffart, Johannes and
Lehmann, Jens",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.40",
doi = "10.18653/v1/2021.eacl-main.40",
pages = "504--514",
abstract = "In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.",
}
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<abstract>In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.</abstract>
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%0 Conference Proceedings
%T CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
%A Kannan Ravi, Manoj Prabhakar
%A Singh, Kuldeep
%A Mulang’, Isaiah Onando
%A Shekarpour, Saeedeh
%A Hoffart, Johannes
%A Lehmann, Jens
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F kannan-ravi-etal-2021-cholan
%X In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.
%R 10.18653/v1/2021.eacl-main.40
%U https://aclanthology.org/2021.eacl-main.40
%U https://doi.org/10.18653/v1/2021.eacl-main.40
%P 504-514
Markdown (Informal)
[CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://aclanthology.org/2021.eacl-main.40) (Kannan Ravi et al., EACL 2021)
ACL
- Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, and Jens Lehmann. 2021. CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 504–514, Online. Association for Computational Linguistics.