# Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking

arXiv: https://arxiv.org/abs/2101.02394 · HTML: https://mlsanity.com/paper/2101.02394/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Joint Entity Linking with Deep Reinforcement Learning](https://mlsanity.com/paper/1902.00330/) (2019)
- [Named Entity Disambiguation for Noisy Text](https://mlsanity.com/paper/1706.09147/) (2017)
- [Improving Entity Linking by Modeling Latent Entity Type Information](https://mlsanity.com/paper/2001.01447/) (2020)
- [S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking](https://mlsanity.com/paper/1609.08075/) (2016)
- [Distant Learning for Entity Linking with Automatic Noise Detection](https://mlsanity.com/paper/1905.07189/) (2019)
- [Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation](https://mlsanity.com/paper/1908.05762/) (2019)
- [Jointly Embedding Entities and Text with Distant Supervision](https://mlsanity.com/paper/1807.03399/) (2018)

## Built on by (most cited first, 14 of 14)

- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble](https://mlsanity.com/paper/2212.06522/) (2022)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control](https://mlsanity.com/paper/2310.18342/) (2023)
- [A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends](https://mlsanity.com/paper/2302.03512/) (2023)
- [Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition](https://mlsanity.com/paper/2204.05544/) (2022)
- [Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking](https://mlsanity.com/paper/2302.07189/) (2023)
- [Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph](https://mlsanity.com/paper/2401.02212/) (2024)
- [An MRC Framework for Semantic Role Labeling](https://mlsanity.com/paper/2109.06660/) (2021)
- [S2abEL: A Dataset for Entity Linking from Scientific Tables](https://mlsanity.com/paper/2305.00366/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Better than BERT but Worse than Baseline](https://mlsanity.com/paper/2105.05915/) (2021)
- [Learning to Select the Next Reasonable Mention for Entity Linking](https://mlsanity.com/paper/2112.04104/) (2021)

## Abstract (arXiv)

Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framework called Multi-turn Multiple-choice Machine reading comprehension (M3}) to solve the short text EL from a new perspective: a query is generated for each ambiguous mention exploiting its surrounding context, and an option selection module is employed to identify the golden entity from candidates using the query. In this way, M3 framework sufficiently interacts limited context with candidate entities during the encoding process, as well as implicitly considers the dissimilarities inside the candidate bunch in the selection stage. In addition, we design a two-stage verifier incorporated into M3 to address the commonly existed unlinkable problem in short text. To further consider the topical coherence and interdependence among referred entities, M3 leverages a multi-turn fashion to deal with mentions in a sequence manner by retrospecting historical cues. Evaluation shows that our M3 framework achieves the state-of-the-art performance on five Chinese and English datasets for the real-world short text EL.