Reinforcement Learning for Named Entity Recognition from Noisy Data

Abstract

Named entity recognition (NER) is an important task in natural language processing, and is often formalized as a sequence labeling problem. Deep learning becomes the state-of-the-art approach for NER, but the lack of high-quality labeled data remains the bottleneck for model performance. To solve the problem, we employ the distant supervision technique to obtain noisy labeled data, and propose a novel model based on reinforcement learning to revise the wrong labels and distill high-quality data for learning. Specifically, our model consists of two modules, a Tag Modifier and a Tag Predictor. The Tag Modifier corrects the wrong tags with reinforcement learning and feeds the corrected tags into the Tag Predictor. The Tag Predictor makes the sentence-level prediction and returns rewards to the Tag Modifier. Two modules are trained jointly to optimize tag correction and prediction processes. Experiment results show that our model can effectively deal with noises with a small number of correctly labeled data and thus outperform state-of-the-art baselines.