# Named Entity Recognition for Social Media Texts with Semantic Augmentation

arXiv: https://arxiv.org/abs/2010.15458 · HTML: https://mlsanity.com/paper/2010.15458/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 23 of 23)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023)
- [ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders](https://mlsanity.com/paper/2105.01279/) (2021)
- [MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective](https://mlsanity.com/paper/2204.04391/) (2022)
- [Improving Image Captioning via Predicting Structured Concepts](https://mlsanity.com/paper/2311.08223/) (2023)
- [EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification](https://mlsanity.com/paper/2204.11205/) (2022)
- [PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks](https://mlsanity.com/paper/2210.08855/) (2022)
- [The Causal Structure of Domain Invariant Supervised Representation Learning](https://mlsanity.com/paper/2208.06987/) (2022)
- [Detection and Analysis of Offensive Online Content in Hausa Language](https://mlsanity.com/paper/2311.10541/) (2023)
- [Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting](https://mlsanity.com/paper/2204.11406/) (2022)
- [Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition](https://mlsanity.com/paper/2207.03300/) (2022)
- [Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition](https://mlsanity.com/paper/2210.07916/) (2022)
- [Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media](https://mlsanity.com/paper/2210.14163/) (2022)
- [Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence](https://mlsanity.com/paper/2207.00232/) (2022)
- [Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer](https://mlsanity.com/paper/2301.12289/) (2023)
- [The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks](https://mlsanity.com/paper/2504.15160/) (2025)
- [NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2205.05832/) (2022)
- [Hero-Gang Neural Model For Named Entity Recognition](https://mlsanity.com/paper/2205.07177/) (2022)
- [NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension](https://mlsanity.com/paper/2305.03970/) (2023)
- [Relation Extraction Model Based on Semantic Enhancement Mechanism](https://mlsanity.com/paper/2311.02564/) (2023)
- [Leveraging Few-Shot Learning and Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature](https://mlsanity.com/paper/2402.01826/) (2024)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)

## Abstract (arXiv)

Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation. In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account. In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively. Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets.