# Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

arXiv: https://arxiv.org/abs/1601.01343 · HTML: https://mlsanity.com/paper/1601.01343/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2016.

## Built on by (most cited first, 30 of 48)

- [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (2021)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [BERTnesia: Investigating the capture and forgetting of knowledge in BERT](https://mlsanity.com/paper/2106.02902/) (2021)
- [WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types](https://mlsanity.com/paper/2204.06347/) (2022)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge](https://mlsanity.com/paper/2109.12573/) (2021)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections](https://mlsanity.com/paper/2102.13247/) (2021)
- [QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction](https://mlsanity.com/paper/2108.08468/) (2021)
- [Multi-Grained Multimodal Interaction Network for Entity Linking](https://mlsanity.com/paper/2307.09721/) (2023)
- [Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution](https://mlsanity.com/paper/2107.02286/) (2021)
- [Entity-aware Transformers for Entity Search](https://mlsanity.com/paper/2205.00820/) (2022)
- [MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations](https://mlsanity.com/paper/2109.05716/) (2021)
- [Improving Entity Linking through Semantic Reinforced Entity Embeddings](https://mlsanity.com/paper/2106.08495/) (2021)
- [Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection](https://mlsanity.com/paper/2112.06888/) (2021)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Unsupervised Matching of Data and Text](https://mlsanity.com/paper/2112.08776/) (2021)
- [Improving Zero-Shot Entity Retrieval through Effective Dense Representations](https://mlsanity.com/paper/2103.04156/) (2021)
- [Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution](https://mlsanity.com/paper/2108.13530/) (2021)
- [A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models](https://mlsanity.com/paper/2202.13392/) (2022)
- [Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation](https://mlsanity.com/paper/2505.11683/) (2025)
- [Low-Rank Subspaces for Unsupervised Entity Linking](https://mlsanity.com/paper/2104.08737/) (2021)
- [Towards Better Entity Linking with Multi-View Enhanced Distillation](https://mlsanity.com/paper/2305.17371/) (2023)
- [A Systematic Investigation of KB-Text Embedding Alignment at Scale](https://mlsanity.com/paper/2106.01586/) (2021)
- [Learning Entity Linking Features for Emerging Entities](https://mlsanity.com/paper/2208.03877/) (2022)
- [Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings](https://mlsanity.com/paper/2305.12027/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Query Interpretations from Entity-Linked Segmentations](https://mlsanity.com/paper/2105.08581/) (2021)
- [Entity Disambiguation with Entity Definitions](https://mlsanity.com/paper/2210.05648/) (2022)

## Abstract (arXiv)

Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.