Revisiting Sparse Retrieval for Few-shot Entity Linking

Yulin Chen, Zhenran Xu, Baotian Hu, Min Zhang
10/19/2023
cs.CL

No official code found. Best implementation: HITsz-TMG/Sparse-Retrieval-Fewshot-EL (7 stars, last commit 2023-10-21).

Abstract

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific domains. Although dense retrievers have achieved excellent performance on several benchmarks, their performance decreases significantly when only a limited amount of in-domain labeled data is available. In such few-shot setting, we revisit the sparse retrieval method, and propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. For training the extractor, we propose a distant supervision method to automatically generate training data based on overlapping tokens between mention contexts and entity descriptions. Experimental results on the ZESHEL dataset demonstrate that the proposed method outperforms state-of-the-art models by a significant margin across all test domains, showing the effectiveness of keyword-enhanced sparse retrieval.

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Code Implementations(1)

HITsz-TMG/Sparse-Retrieval-Fewshot-ELCommunity100%

Code of EMNLP 2023 paper "Revisiting Sparse Retrieval for Few-shot Entity Linking".

70PythonOct 17, 20233 years ago
entity-linkingentity-retrieval

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Cite this paper

@article{chen2023revisiting,
  title  = {Revisiting Sparse Retrieval for Few-shot Entity Linking},
  author = {Yulin Chen and Zhenran Xu and Baotian Hu and Min Zhang},
  year   = {2023},
  eprint = {2310.12444},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.12444v1}
}

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