# Adversarial Learning for Chinese NER from Crowd Annotations

arXiv: https://arxiv.org/abs/1801.05147 · HTML: https://mlsanity.com/paper/1801.05147/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition](https://mlsanity.com/paper/2105.14980/) (2021)
- [CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals](https://mlsanity.com/paper/2106.05544/) (2021)
- [A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition](https://mlsanity.com/paper/2305.12485/) (2023)

## Abstract (arXiv)

To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. In this paper, we propose an approach to performing crowd annotation learning for Chinese Named Entity Recognition (NER) to make full use of the noisy sequence labels from multiple annotators. Inspired by adversarial learning, our approach uses a common Bi-LSTM and a private Bi-LSTM for representing annotator-generic and -specific information. The annotator-generic information is the common knowledge for entities easily mastered by the crowd. Finally, we build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we create two data sets for Chinese NER tasks from two domains. The experimental results show that our system achieves better scores than strong baseline systems.