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.

Contract analysis with AI techniques can significantly ease the work for humans. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). We present a novel Text-Of-Interest (TOI) convolutional neural network for the ETIP solution. We introduce a TOI pooling layer to replace traditional pooling layer for processing the nested phrasal or clausal elements in insurance policies. The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes. The computation of backpropagation through TOI pooling is also demonstrated in the paper. In addition, a location regressor is trained to improve the precision of element localization, called TOI-CNN+LR. In the detection, we devise a novel non-maximum suppression method with the fusion of length and score metrics, called LS-NMS. A large Chinese insurance contract dataset was collected to test the performance of the proposed method. An extensive set of experiments is performed to investigate how TOI-CNN+LR can work effectively in insurance elements tagging and outperforms other state-of-the-art nested NER models.