The selection of text features is a fundamental task and plays an important role in digital document analysis. Conventional methods in text feature extraction necessitate indigenous features. Obtaining an efficient feature is an extensive process, but a new and real-time representation of features in text data is a challenging task. Deep learning is making inroads in digital document mining. A significant distinction between deep learning and traditional methods is that deep learning learns features in a digital document in an automatic manner. In this paper, logistic regression and deep dependency parsing (LR-DDP) methods are proposed. The logistic regression token generation model generates robust tokens by means of Napierian grammar. With the robust generated tokens, a deep transition-based dependency parsing using duplex long short-term memory is designed. Experimental results demonstrate that our dependency parser achieves comparable performance in terms of digital document parsing accuracy, parsing time and overhead when compared to existing methods. Hence, these methods are found to be computationally efficient and accurate.

文本特征的选择是一项基本任务，在数字文档分析中起着重要作用。文本特征提取中的传统方法需要本地特征。获得有效的特征是一个广泛的过程，但文本数据中特征的新的实时表示是一项具有挑战性的任务。深度学习正在数字文档挖掘领域取得进展。深度学习与传统方法的一个显着区别是深度学习以自动方式学习数字文档中的特征。在本文中，提出了逻辑回归和深度依赖解析（LR-DDP）方法。逻辑回归标记生成模型通过纳皮尔文法生成稳健的标记。凭借强大的生成令牌，设计了一种使用双工长短期记忆的基于深度转换的依赖解析。实验结果表明，与现有方法相比，我们的依赖解析器在数字文档解析精度、解析时间和开销方面实现了可比的性能。因此，发现这些方法在计算上是有效且准确的。