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

Abstract

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.

References in corpus (1)

• Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning

Cited by in corpus (6)

• CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
• Self-attention-based BiGRU and capsule network for named entity recognition
• FLAT: Chinese NER Using Flat-Lattice Transformer
• Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
• A Chinese Corpus for Fine-grained Entity Typing
• Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text