# Cluster-based Mention Typing for Named Entity Disambiguation

arXiv: https://arxiv.org/abs/2109.11389 · HTML: https://mlsanity.com/paper/2109.11389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 88 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [Improving Entity Linking by Modeling Latent Relations between Mentions](https://mlsanity.com/paper/1804.10637/) (2018)
- [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://mlsanity.com/paper/1601.01343/) (2016)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [Joint Entity Linking with Deep Reinforcement Learning](https://mlsanity.com/paper/1902.00330/) (2019)
- [Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking](https://mlsanity.com/paper/1807.05127/) (2018)
- [Collective Entity Disambiguation with Structured Gradient Tree Boosting](https://mlsanity.com/paper/1802.10229/) (2018)
- [Neural Cross-Lingual Entity Linking](https://mlsanity.com/paper/1712.01813/) (2017)
- [Boosting Entity Linking Performance by Leveraging Unlabeled Documents](https://mlsanity.com/paper/1906.01250/) (2019)
- [Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All](https://mlsanity.com/paper/1802.01074/) (2018)
- [Corpus-level Fine-grained Entity Typing Using Contextual Information](https://mlsanity.com/paper/1606.07901/) (2016)
- [Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods](https://mlsanity.com/paper/1504.06658/) (2015)
- [Probabilistic Bag-Of-Hyperlinks Model for Entity Linking](https://mlsanity.com/paper/1509.02301/) (2015)
- [Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)](https://mlsanity.com/paper/1603.03112/) (2016)
- [Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities](https://mlsanity.com/paper/1701.02025/) (2017)

## Abstract (arXiv)

An entity mention in text such as "Washington" may correspond to many different named entities such as the city "Washington D.C." or the newspaper "Washington Post." The goal of named entity disambiguation is to identify the mentioned named entity correctly among all possible candidates. If the type (e.g. location or person) of a mentioned entity can be correctly predicted from the context, it may increase the chance of selecting the right candidate by assigning low probability to the unlikely ones. This paper proposes cluster-based mention typing for named entity disambiguation. The aim of mention typing is to predict the type of a given mention based on its context. Generally, manually curated type taxonomies such as Wikipedia categories are used. We introduce cluster-based mention typing, where named entities are clustered based on their contextual similarities and the cluster ids are assigned as types. The hyperlinked mentions and their context in Wikipedia are used in order to obtain these cluster-based types. Then, mention typing models are trained on these mentions, which have been labeled with their cluster-based types through distant supervision. At the named entity disambiguation phase, first the cluster-based types of a given mention are predicted and then, these types are used as features in a ranking model to select the best entity among the candidates. We represent entities at multiple contextual levels and obtain different clusterings (and thus typing models) based on each level. As each clustering breaks the entity space differently, mention typing based on each clustering discriminates the mention differently. When predictions from all typing models are used together, our system achieves better or comparable results based on randomization tests with respect to the state-of-the-art levels on four defacto test sets.