# CMNER: A Chinese Multimodal NER Dataset based on Social Media

arXiv: https://arxiv.org/abs/2402.13693 · HTML: https://mlsanity.com/paper/2402.13693/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://mlsanity.com/paper/2211.01335/) (2022)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Improving Entity Linking by Modeling Latent Relations between Mentions](https://mlsanity.com/paper/1804.10637/) (2018)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [Multimodal Named Entity Recognition for Short Social Media Posts](https://mlsanity.com/paper/1802.07862/) (2018)
- [MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER](https://mlsanity.com/paper/2108.13655/) (2021)
- [Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language](https://mlsanity.com/paper/2004.12440/) (2020)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Neural Segmental Hypergraphs for Overlapping Mention Recognition](https://mlsanity.com/paper/1810.01817/) (2018)
- [Entity Projection via Machine Translation for Cross-Lingual NER](https://mlsanity.com/paper/1909.05356/) (2019)
- [Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER](https://mlsanity.com/paper/1909.00153/) (2019)
- [ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition](https://mlsanity.com/paper/2112.06482/) (2021)
- [AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER](https://mlsanity.com/paper/2106.02300/) (2021)
- [MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding](https://mlsanity.com/paper/2211.14739/) (2022)
- [Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning](https://mlsanity.com/paper/2305.13628/) (2023)
- [Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling](https://mlsanity.com/paper/1908.11326/) (2019)
- [Can images help recognize entities? A study of the role of images for Multimodal NER](https://mlsanity.com/paper/2010.12712/) (2020)

## Built on by (most cited first, 2 of 2)

- [MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image](https://mlsanity.com/paper/2412.00060/) (2024)
- [Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads](https://mlsanity.com/paper/2604.23009/) (2026)

## Abstract (arXiv)

Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images. Nonetheless, a notable paucity of data for Chinese MNER has considerably impeded the progress of this natural language processing task within the Chinese domain. Consequently, in this study, we compile a Chinese Multimodal NER dataset (CMNER) utilizing data sourced from Weibo, China's largest social media platform. Our dataset encompasses 5,000 Weibo posts paired with 18,326 corresponding images. The entities are classified into four distinct categories: person, location, organization, and miscellaneous. We perform baseline experiments on CMNER, and the outcomes underscore the effectiveness of incorporating images for NER. Furthermore, we conduct cross-lingual experiments on the publicly available English MNER dataset (Twitter2015), and the results substantiate our hypothesis that Chinese and English multimodal NER data can mutually enhance the performance of the NER model.