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首页> 外文期刊>ETRI journal >Deep recurrent neural networks with word embeddings for Urdu named entity recognition
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Deep recurrent neural networks with word embeddings for Urdu named entity recognition

机译：具有Word Embeddings的深度经常性神经网络，用于URDU命名实体识别

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Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the “context windows” of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.
机译：命名实体识别（NER）继续成为自然语言处理中的重要任务，因为它在信息提取和机器翻译中的子任务和/或子问题。在乌尔都语语言处理中，这是一项非常艰巨的任务。本文提出了各种深度经常性神经网络（DRNN）学习模型，单词嵌入。实验结果表明，他们改善了当前最先进的URDU方法。评估的DRRN模型包括长短期内存的前向和双向扩展，并通过时间方法进行后传播。所提出的模型考虑依赖语言依赖性功能，例如语音部分标签，以及语言无关的功能，例如单词的“上下文窗口”。使用三个数据集演示了在URDU中嵌入NER的DRNN模型的有效性。结果表明，所提出的方法显着优于先前有条件的随机场和人工神经网络方法。使用所提出的深度学习方法在三个基准数据集上实现的最佳F测量值分别为81.1％，79.94％和63.21％。

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