Employing Auto-annotated Data for Person Name Recognition in Judgment Documents

In the last decades, named entity recognition has been extensively studied with various supervised learning approaches depend on massive labeled data. In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (in a small size) for person name recognition. Specifically, our approach first develops an auxiliary Long Short-Term Memory (LSTM) representation by training the auto-annotated data and then leverages the auxiliary LSTM representation to boost the performance of classifier trained on the human-annotated data. Empirical studies demonstrate the effectiveness of our proposed approach to person name recognition in judgment documents with both human-annotated and auto-annotated data.