Person Name Recognition with Fine-grained Annotation

Abstract

Person names are essential and important entities in the Named Entity Recognition (NER) task. Traditional NER models have shown success in recognising well-formed person names from text with consistent and complete syntax, such as news articles. However, user-generated text such as academic homepages, academic resumes, articles in online forums and social media may contain lots of free-form text with incomplete syntax including person names with various forms. This brings significant challenges for the NER task. In this paper, we address person name recognition in this context by proposing a fine-grained annotation scheme based on anthroponymy together with a new machine learning model to perform the task of person name recognition. Specifically, our proposed name annotation scheme labels fine-grained name forms including first, middle, or last names, and whether the name is a full name or initial. Such fine-grained annotations offer richer training signals for models to learn person name patterns in free-form text. We then propose a Co-guided Neural Network (CogNN) model to take full advantage of the fine-grained annotations. CogNN uses co-attention and gated fusion to co-guide two jointly trained neural networks, each focusing on different dimensions of the name forms. Experiments on academic homepages and news articles demonstrate that our annotation scheme together with the CogNN model outperforms state-of-the-art significantly.