# Analysis of Named Entity Recognition and Linking for Tweets

arXiv: https://arxiv.org/abs/1410.7182 · HTML: https://mlsanity.com/paper/1410.7182/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2014.

## Built on by (most cited first, 20 of 20)

- [CM3: A Causal Masked Multimodal Model of the Internet](https://mlsanity.com/paper/2201.07520/) (2022)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [Instructed Language Models with Retrievers Are Powerful Entity Linkers](https://mlsanity.com/paper/2311.03250/) (2023)
- [ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget](https://mlsanity.com/paper/2408.00103/) (2024)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres](https://mlsanity.com/paper/2109.07449/) (2021)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Contextual Augmentation for Entity Linking using Large Language Models](https://mlsanity.com/paper/2510.18888/) (2025)
- [Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines](https://mlsanity.com/paper/2102.10966/) (2021)
- [TMR: Evaluating NER Recall on Tough Mentions](https://mlsanity.com/paper/2103.12312/) (2021)
- [TweetNERD -- End to End Entity Linking Benchmark for Tweets](https://mlsanity.com/paper/2210.08129/) (2022)
- [Entity Disambiguation via Fusion Entity Decoding](https://mlsanity.com/paper/2404.01626/) (2024)
- [Evaluating end-to-end entity linking on domain-specific knowledge bases: Learning about ancient technologies from museum collections](https://mlsanity.com/paper/2305.14588/) (2023)
- [A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems](https://mlsanity.com/paper/2305.14937/) (2023)
- [Crisis talk: analysis of the public debate around the energy crisis and cost of living](https://mlsanity.com/paper/2402.18043/) (2024)
- [CLIMATELI: Evaluating Entity Linking on Climate Change Data](https://mlsanity.com/paper/2406.16732/) (2024)

## Abstract (arXiv)

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.