# Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark

arXiv: https://arxiv.org/abs/2207.13005 · HTML: https://mlsanity.com/paper/2207.13005/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Entities as Experts: Sparse Memory Access with Entity Supervision](https://mlsanity.com/paper/2004.07202/) (2020)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://mlsanity.com/paper/1808.09920/) (2018)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP](https://mlsanity.com/paper/2106.06830/) (2021)
- [Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking](https://mlsanity.com/paper/2005.14253/) (2020)
- [Neural Cross-Lingual Entity Linking](https://mlsanity.com/paper/1712.01813/) (2017)
- [Learning Cross-Context Entity Representations from Text](https://mlsanity.com/paper/2001.03765/) (2020)
- [Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing](https://mlsanity.com/paper/2108.10949/) (2021)
- [TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval](https://mlsanity.com/paper/2204.08173/) (2022)
- [Joint Multilingual Supervision for Cross-lingual Entity Linking](https://mlsanity.com/paper/1809.07657/) (2018)
- [Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA](https://mlsanity.com/paper/2003.03875/) (2020)
- [Towards Zero-resource Cross-lingual Entity Linking](https://mlsanity.com/paper/1909.13180/) (2019)
- [Feature Engineering for Knowledge Base Construction](https://mlsanity.com/paper/1407.6439/) (2014)

## Built on by (most cited first, 2 of 2)

- [Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever](https://mlsanity.com/paper/2308.03365/) (2023)
- [MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2310.11088/) (2023)

## Abstract (arXiv)

Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task.