# Neural Cross-Lingual Entity Linking

arXiv: https://arxiv.org/abs/1712.01813 · HTML: https://mlsanity.com/paper/1712.01813/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2017.

## Built on by (most cited first, 15 of 15)

- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Visual Named Entity Linking: A New Dataset and A Baseline](https://mlsanity.com/paper/2211.04872/) (2022)
- [Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem](https://mlsanity.com/paper/2204.05990/) (2022)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [Multilingual End to End Entity Linking](https://mlsanity.com/paper/2306.08896/) (2023)
- [Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations](https://mlsanity.com/paper/2104.02667/) (2021)
- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)
- [Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark](https://mlsanity.com/paper/2207.13005/) (2022)
- [ReFIT: Relevance Feedback from a Reranker during Inference](https://mlsanity.com/paper/2305.11744/) (2023)
- [An Entity Linking Agent for Question Answering](https://mlsanity.com/paper/2508.03865/) (2025)
- [Cluster-based Mention Typing for Named Entity Disambiguation](https://mlsanity.com/paper/2109.11389/) (2021)
- [Efficient Entity Candidate Generation for Low-Resource Languages](https://mlsanity.com/paper/2206.15163/) (2022)
- [Review of coreference resolution in English and Persian](https://mlsanity.com/paper/2211.04428/) (2022)
- [Persian Pronoun Resolution: Leveraging Neural Networks and Language Models](https://mlsanity.com/paper/2405.10714/) (2024)

## Abstract (arXiv)

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.