In well-spaced Korean sentences, morphological analysis is the first step in natural language processing, in which a Korean sentence is segmented into a sequence of morphemes and the parts of speech of the segmented morphemes are determined. Named entity recognition is a natural language processing task carried out to obtain morpheme sequences with specific meanings, such as person, location, and organization names. Although morphological analysis and named entity recognition are closely associated with each other, they have been independently studied and have exhibited the inevitable error propagation problem. Hence, we propose an integrated model based on label attention networks that simultaneously performs morphological analysis and named entity recognition. The proposed model comprises two layers of neural network models that are closely associated with each other. The lower layer performs a morphological analysis, whereas the upper layer performs a named entity recognition. In our experiments using a public gold-labeled dataset, the proposed model outperformed previous state-of-the-art models used for morphological analysis and named entity recognition. Furthermore, the results indicated that the integrated architecture could alleviate the error propagation problem.

中文翻译：

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基于标签注意网络的朝鲜语形态分析与命名实体识别集成模型

在间距良好的朝鲜语句子中，形态分析是自然语言处理的第一步，其中将朝鲜语句子分割为一系列的语素，并确定分割后的语素的词性。命名实体识别是一种自然语言处理任务，用于获取具有特定含义的语素序列，例如人，位置和组织名称。尽管形态分析和命名实体识别彼此紧密相关，但是它们已经被独立研究并且展现了不可避免的错误传播问题。因此，我们提出了一个基于标签关注网络的集成模型，该模型同时执行形态分析和命名实体识别。所提出的模型包括彼此紧密关联的两层神经网络模型。下层执行形态分析，而上层执行命名实体识别。在我们使用公开金标数据集的实验中，提出的模型优于先前用于形态分析和命名实体识别的最新模型。此外，结果表明，集成架构可以缓解错误传播问题。