Joint Learning of Token Context and Span Feature for Span-Based Nested NER
Lin Sun, Yuxuan Sun, Fule Ji, Chi Wang
Abstract
Nested named entity recognition (NER) is a linguistic phenomenon that has received increasing attention in the NLP community. In this article, we propose a novel joint learning network of the token context, and span feature (TCSF) for nested NER. Our model is a combination of token context network (TCN) for token learning, and deep residual convolutional neural network (CNN), and span relation network (SRN) for span learning. Span features are represented at both the token level, and span relation level. The span relation representation of SRN is trained on the similarity of span, and its positional features by attention weights. The IoU metric is employed for span filtering, and analysis of the overlapping adjacent spans. Moreover, we propose a novel head-inner-tail (HIP T) operation to extend the inner features in spans for a fixed-length representation. TCSF is a fully end-to-end entity recognition model. We perform a set of comprehensive experiments, including an ablation study on the ACE, and GENIA datasets, and show state-of-the-art performance without, and with the pretrained language models.