So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods mainly include span-based and sequence-to-sequence models, where unfortunately the former merely focus on boundary identification and the latter may suffer from exposure bias. In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. The architecture resolves the kernel bottleneck of unified NER by effectively modeling the neighboring relations between entity words with Next-Neighboring-Word (NNW) and Tail-Head-Word-* (THW-*) relations. Based on the W^2NER scheme we develop a neural framework, in which the unified NER is modeled as a 2D grid of word pairs. We then propose multi-granularity 2D convolutions for better refining the grid representations. Finally, a co-predictor is used to sufficiently reason the word-word relations. We perform extensive experiments on 14 widely-used benchmark datasets for flat, overlapped, and discontinuous NER (8 English and 6 Chinese datasets), where our model beats all the current top-performing baselines, pushing the state-of-the-art performances of unified NER.
翻译:到目前为止,命名实体识别(NER)一直涉及三大类,包括统一、重叠(嵌套)和不连续的NER,这些类型大多是单独研究的。最近,人们对统一NER的兴趣日益浓厚,与单一模式同时处理上述三个工作。目前的最佳方法主要包括基于跨和顺序顺序的模型,不幸的是,前者只是侧重于边界识别,而后者可能存在接触偏差。在这项工作中,我们提出了一个新的替代方案,将统一NER建为统一词词关系分类模式,即W&2NER。该建筑解决了统一NER的内核瓶颈。通过有效地模拟与下-Neighboring-Word(NNW)和Tattle-head-Word-*(THW-*)关系之间的实体词际关系。根据W2NER计划,我们开发了一个神经框架,将统一NER建为2D的词源关系分类。我们随后提出了多调调调制2D Convolution 2, comvolutions the mainal