# F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media

arXiv: https://arxiv.org/abs/1611.04234 · HTML: https://mlsanity.com/paper/1611.04234/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2016.

## Built on by (most cited first, 11 of 11)

- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning](https://mlsanity.com/paper/2111.04198/) (2021)
- [Accelerating BERT Inference for Sequence Labeling via Early-Exit](https://mlsanity.com/paper/2105.13878/) (2021)
- [MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2107.05418/) (2021)
- [DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling](https://mlsanity.com/paper/2109.08818/) (2021)
- [SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.05632/) (2022)
- [MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition](https://mlsanity.com/paper/2109.07877/) (2021)
- [Statistical Dataset Evaluation: Reliability, Difficulty, and Validity](https://mlsanity.com/paper/2212.09272/) (2022)
- [NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2205.05832/) (2022)
- [Application of Data Encryption in Chinese Named Entity Recognition](https://mlsanity.com/paper/2208.14627/) (2022)
- [Unified Lattice Graph Fusion for Chinese Named Entity Recognition](https://mlsanity.com/paper/2312.16917/) (2023)

## Abstract (arXiv)

We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44% improvement over previous state-of-the-art result.