MISS: Multiple information span scoring for Chinese named entity recognition

Abstract

Named entity recognition (NER) has drawn much attention from researchers. In Chinese text, characters carry rich contextual and regularity-based information. In most previous works on Chinese NER, a model excavates boundary features of phrase spans, yet the token information within spans and relationship between adjacent spans are neglected, which leads to insufficient feature representations and thereby limits model performance. In this study, we construct a span-based NER model named MISS (Multiple Information Span Scoring). The model consists of two major modules: (1) a span extractor for type-independent entity extraction, where the relative position information is introduced into sequence representations; and (2) a span classifier that fuses boundary and internal information into span representations for enhanced span scoring. In the span classifier, we also employ a convolutional layer to conduct cross-span interaction, which rectifies the classification scores. Entity predictions are decoded from the sum of scores computed by two modules. Our method is simple and effective. Without any external resources, MISS achieves considerable improvement on four benchmark datasets. Moreover, the ablation experiments have demonstrated the effectiveness of each component in our model.