# An Unsupervised Language-Independent Entity Disambiguation Method and its Evaluation on the English and Persian Languages

arXiv: https://arxiv.org/abs/2102.00395 · HTML: https://mlsanity.com/paper/2102.00395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://mlsanity.com/paper/1601.01343/) (2016)
- [Improving Candidate Generation for Low-resource Cross-lingual Entity Linking](https://mlsanity.com/paper/2003.01343/) (2020)
- [MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach](https://mlsanity.com/paper/1707.05288/) (2017)
- [Probabilistic Bag-Of-Hyperlinks Model for Entity Linking](https://mlsanity.com/paper/1509.02301/) (2015)
- [Entity Linking with people entity on Wikipedia](https://mlsanity.com/paper/1705.01042/) (2017)

## Abstract (arXiv)

Entity Linking is one of the essential tasks of information extraction and natural language understanding. Entity linking mainly consists of two tasks: recognition and disambiguation of named entities. Most studies address these two tasks separately or focus only on one of them. Moreover, most of the state-of-the -art entity linking algorithms are either supervised, which have poor performance in the absence of annotated corpora or language-dependent, which are not appropriate for multi-lingual applications. In this paper, we introduce an Unsupervised Language-Independent Entity Disambiguation (ULIED), which utilizes a novel approach to disambiguate and link named entities. Evaluation of ULIED on different English entity linking datasets as well as the only available Persian dataset illustrates that ULIED in most of the cases outperforms the state-of-the-art unsupervised multi-lingual approaches.