Complex Named Entity Recognition via Deep Multi-task Learning from Scratch

Abstract

Named Entity Recognition (NER) is the preliminary task in many basic NLP technologies and deep neural networks has shown their promising opportunities in NER task. However, the NER tasks covered in previous work are relatively simple, focusing on classic entity categories (Persons, Locations, Organizations) and failing to meet the requirements of newly-emerging application scenarios, where there exist more informal entity categories or even hierarchical category structures. In this paper, we propose a multi-task learning based subtask learning strategy to combat the complexity of modern NER tasks. We conduct experiments on a complex Chinese NER task, and the experimental results demonstrate the effectiveness of our approach.