A Weak Supervision Approach with Adversarial Training for Named Entity Recognition

Named entity recognition (NER) is a basic task of natural language processing (NLP), whose purpose is to identify named entities such as the names of persons, places, and organizations in the corpus. Utilizing neural networks for feature extraction, followed by conditional random field (CRF) layer decoding, is effective for the NER task. However, achieving reliable results using neural networks generally requires a large amount of labeled data and the acquisition of high-quality labeled data is costly. To obtain a better NER effect without labeled data, we propose a weak supervision approach with adversarial training (WSAT). WSAT obtains supervised information and domain knowledge through labeling functions, including external knowledge bases, heuristic functions, and generic entity recognition tools. The labeled results are aggregated through the linked hidden Markov model (linked HMM), and adversarial training strategies are added when using the aggregated results for training. We evaluate WSAT on two real-world datasets. When compared to rival algorithms, the F1 values are improved by approximately 2% and 1% on the MSRA and Resume NER datasets, respectively.