Natural language processing is an application of a computational technique that allows the machine to process human language. One of the primary tasks of NLP is information extraction that aims to capture important information from the text. Nowadays, the fast-growing web contains a large amount of textual information, requires a technique to extract relevant information. The entity recognition task is a type of information extraction that attempts to find and classify named entities appearing in the unstructured text document. The traditional coarse-grained entity recognition systems often define a less number of pre-defined named entity categories such as person, location, organization, and date. The fine-grained entity type classification model focused to classify the target entities into fine-grained types. Most of the recent works are accomplished with the help of Bidirectional LSTM with an attention mechanism. But due to the complex structure of bidirectional LSTM, these models consume an enormous amount of time for the training process. The existing attention mechanisms are incapable to pick up the correlation between the new word and the previous context. The proposed system resolves this issue by utilizing bidirectional GRU with the self-attention mechanism. The experiment result shows that the novel approach outperforms state-of-the-art methods.

中文翻译：

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使用GRU进行自我关注的细粒度实体类型分类

自然语言处理是允许计算机处理人类语言的计算技术的一种应用。NLP的主要任务之一是信息提取，旨在从文本中捕获重要信息。如今，快速发展的网站包含大量文本信息，需要一种提取相关信息的技术。实体识别任务是一种信息提取类型，它试图查找和分类出现在非结构化文本文档中的命名实体。传统的粗粒度实体识别系统通常会定义较少数量的预定义命名实体类别，例如人员，位置，组织和日期。细粒度实体类型分类模型着重于将目标实体分类为细粒度类型。最近的大部分工作都是在具有注意力机制的双向LSTM的帮助下完成的。但是由于双向LSTM的复杂结构，这些模型在训练过程中会花费大量时间。现有的注意力机制无法获取新词与先前上下文之间的相关性。拟议的系统通过使用带有自注意机制的双向GRU解决了此问题。实验结果表明，该新方法优于最新方法。现有的注意力机制无法获取新词与先前上下文之间的相关性。所提出的系统通过利用具有自注意机制的双向GRU解决了此问题。实验结果表明，该新方法优于最新方法。现有的注意力机制无法获取新词与先前上下文之间的相关性。所提出的系统通过利用具有自注意机制的双向GRU解决了此问题。实验结果表明，该新方法优于最新方法。