多场景文本的细粒度命名实体识别

命名实体识别一直是数据挖掘领域的经典问题之一,尤其随着网络数据的剧增,如果能对多来源的文本数据进行多领域、细粒度的命名实体识别,显然能够为很多的数据挖掘应用提供支持。该文提出一种多领域、细粒度的命名实体识别方法,利用网络词典回标文本数据获得了大量的粗糙训练文本。为防止训练文本中的噪声干扰命名实体识别的结果,该算法将命名实体识别的过程划分为两个阶段,第一个阶段先获得命名实体的领域标签,之后利用命名实体的上下文确定命名实体的细粒度标签。实验结果显示,该文提出的方法使F1值在全领域上平均值达到了80%左右。

Fine-grained Named Entity Recognition for Multi-scenario

Name entity recognition is a classical research issue in data mining community. To recognize the entities in multi-domain with fine-grained labels, we propose a method of utilizes web thesaurus to annotate web data automatically to acquire large-scale training corpus. To minimize the influence of the noises in training corpus, we design a two-phase entity recognition method. First, the entity’s domain label is obtained. After that, the context of each recognized entity is used to determine the fine-grained label for one entity. Experimental results demonstrate that the proposed method can obtain high accuracy on entity recognition in multiple domains.