# Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

arXiv: https://arxiv.org/abs/1509.02301 · HTML: https://mlsanity.com/paper/1509.02301/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2015.

## Built on by (most cited first, 4 of 4)

- [An Entity Linking Agent for Question Answering](https://mlsanity.com/paper/2508.03865/) (2025)
- [An Unsupervised Language-Independent Entity Disambiguation Method and its Evaluation on the English and Persian Languages](https://mlsanity.com/paper/2102.00395/) (2021)
- [Cluster-based Mention Typing for Named Entity Disambiguation](https://mlsanity.com/paper/2109.11389/) (2021)
- [Cross-Domain Neural Entity Linking](https://mlsanity.com/paper/2210.15616/) (2022)

## Abstract (arXiv)

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods.