Designing a Bangla parser using non-deterministic push down automata

Md. Mostafizur Rahman, Md. Abdulla-Al-Sun, K. Hasan, M. Shuvo
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引用次数: 3

Abstract

The goal of language processing is to make machines be able to read human language comprehensively and use human language to communicate with human beings. To achieve this goal, the first step is to parse the sentence structure correctly. In this paper, we propose a parsing technique for Bangla Language based on Non-Deterministic Push-Down Automata (NPDA). The NPDA parser takes the Context Free Grammars (CFG) of Bangla Language for preprocessing. The NPDA is efficient especially when large number of CFGs needs to be processed. The predictive parser needs to generate parse table from the CFG if the number of CFGs are large then it is difficult to construct such a big parse table. The NPDA does not require constructing any parse table and it can process the CFG directly. The parser can detect Bangla sentences of all forms whether they are syntactically and grammatically correct or not. Finally, we compare these two parsers with their complexity and efficiency issues. Comparing with these important issues, the NPDA parser gives the better result than predictive parser. Sufficient examples and figures are described to explain the parsing idea.
设计一个使用非确定性下推自动机的孟加拉语解析器
语言处理的目标是使机器能够全面阅读人类语言,并使用人类语言与人类进行交流。要实现这一目标,第一步是正确解析句子结构。本文提出了一种基于非确定性下推自动机(NPDA)的孟加拉语解析技术。NPDA解析器采用孟加拉语的上下文无关语法(CFG)进行预处理。NPDA是高效的,特别是当需要处理大量的cfg时。预测解析器需要从CFG生成解析表,如果CFG的数量很大,那么很难构建如此大的解析表。NPDA不需要构造任何解析表,它可以直接处理CFG。语法分析器可以检测所有形式的孟加拉语句子,无论它们在语法和语法上是否正确。最后,我们比较了这两种解析器的复杂性和效率问题。比较这些重要问题,NPDA解析器比预测解析器给出了更好的结果。文中描述了足够的例子和图形来解释解析思想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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