论文标题
全意识的实体候选网络命名实体识别
Entity Candidate Network for Whole-Aware Named Entity Recognition
论文作者
论文摘要
命名实体识别（NER）是自然语言处理（NLP）的至关重要的上游任务。传统的标签方案方法提供了一种识别，该识别无法满足许多下游任务的需求，例如COREFERENT分辨率。同时，TAG方案的方法忽略了实体的连续性。受到计算机视觉（CV）中一阶段对象检测模型的启发，本文提出了一种新的NOTAG方案，即全意识检测，这使NER成为对象检测任务。同时，本文介绍了一种新颖的模型，实体候选网络（ECNET）和一个特定的卷积网络，自适应上下文卷积网络（ACCN），以融合多规模上下文并在每个位置进行编码实体信息。 ECNET根据实体损失确定每个位置的命名实体的完整跨度及其类型。此外，eCNET是可以调节的，而最高的召回率和最高召回率是不可调节的，而标签方案的方法不是。 Conll 2003英语数据集和WNUT 2017数据集的实验结果表明，ECNET的表现优于其他先前的最新方法。
Named Entity Recognition (NER) is a crucial upstream task in Natural Language Processing (NLP). Traditional tag scheme approaches offer a single recognition that does not meet the needs of many downstream tasks such as coreference resolution. Meanwhile, Tag scheme approaches ignore the continuity of entities. Inspired by one-stage object detection models in computer vision (CV), this paper proposes a new no-tag scheme, the Whole-Aware Detection, which makes NER an object detection task. Meanwhile, this paper presents a novel model, Entity Candidate Network (ECNet), and a specific convolution network, Adaptive Context Convolution Network (ACCN), to fuse multi-scale contexts and encode entity information at each position. ECNet identifies the full span of a named entity and its type at each position based on Entity Loss. Furthermore, ECNet is regulable between the highest precision and the highest recall, while the tag scheme approaches are not. Experimental results on the CoNLL 2003 English dataset and the WNUT 2017 dataset show that ECNet outperforms other previous state-of-the-art methods.
