# Efficient Entity Candidate Generation for Low-Resource Languages

arXiv: https://arxiv.org/abs/2206.15163 · HTML: https://mlsanity.com/paper/2206.15163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [Are All Good Word Vector Spaces Isomorphic?](https://mlsanity.com/paper/2004.04070/) (2020)
- [Neural Cross-Lingual Entity Linking](https://mlsanity.com/paper/1712.01813/) (2017)
- [Charagram: Embedding Words and Sentences via Character n-grams](https://mlsanity.com/paper/1607.02789/) (2016)
- [Zero-shot Neural Transfer for Cross-lingual Entity Linking](https://mlsanity.com/paper/1811.04154/) (2018)
- [Knowledge-aware Pronoun Coreference Resolution](https://mlsanity.com/paper/1907.03663/) (2019)
- [Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary](https://mlsanity.com/paper/1705.00424/) (2017)
- [Learning Distributed Representations of Texts and Entities from Knowledge Base](https://mlsanity.com/paper/1705.02494/) (2017)
- [Improving Candidate Generation for Low-resource Cross-lingual Entity Linking](https://mlsanity.com/paper/2003.01343/) (2020)
- [Joint Multilingual Supervision for Cross-lingual Entity Linking](https://mlsanity.com/paper/1809.07657/) (2018)
- [One for All: Towards Language Independent Named Entity Linking](https://mlsanity.com/paper/1712.01797/) (2017)
- [Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages](https://mlsanity.com/paper/1809.07807/) (2018)

## Abstract (arXiv)

Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Nevertheless, it has often been overlooked in the monolingual English entity linking literature, as naive approaches obtain very good performance. Unfortunately, the existing approaches for English cannot be successfully transferred to poorly resourced languages. This paper constitutes an in-depth analysis of the candidate generation problem in the context of cross-lingual entity linking with a focus on low-resource languages. Among other contributions, we point out limitations in the evaluation conducted in previous works. We introduce a characterization of queries into types based on their difficulty, which improves the interpretability of the performance of different methods. We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches. A thorough empirical analysis on 9 real-world datasets under 2 evaluation settings shows that our simple solution outperforms the state-of-the-art approach in terms of both quality and efficiency for almost all datasets and query types.