Complexity Analysis of Chinese Text Based on the Construction Grammar Theory and Deep Learning

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-04-10 DOI:10.1145/3625390

Changlin Wu, Changan Wu

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引用次数: 0

Abstract

Due to the complexity of Chinese and the differences between Chinese and English, the application of Chinese text in the digital field has a certain complexity. Taking Chinese text in Open Relation Extraction (ORE) as the research object, the complexity of Chinese text is analyzed. An extraction system of word vectors based on construction grammar theory and Deep Learning (DL) is constructed to achieve smooth extraction of Chinese text. The work of this paper mainly includes the following aspects. To study the application of DL in the complexity analysis of Chinese text based on construction grammar, firstly, the connotation of construction grammar and its role in Chinese text analysis are explored. Secondly, from the perspective of the ORE of word vectors in language analysis, an ORE model based on word vectors is implemented. Moreover, an extraction method based on the distance of word vectors is proposed. The test results show that the F1 value of the proposed algorithm is 67% on the public WEB-500 and NYT-500 datasets, which is superior to other similar text extraction algorithms. When the recall rate is more than 30%, the accuracy of the proposed method is higher than several other latest language analysis systems. This indicates that the proposed Chinese text extraction system based on the DL algorithm and construction grammar theory has advantages in complexity analysis and can provide a new research idea for Chinese text analysis.

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基于构造语法理论和深度学习的中文文本复杂性分析

由于中文的复杂性和中英文的差异，中文文本在数字领域的应用具有一定的复杂性。以开放关系提取（ORE）中的中文文本为研究对象，分析了中文文本的复杂性。构建了基于构词法理论和深度学习（DL）的词向量提取系统，实现了中文文本的平滑提取。本文的工作主要包括以下几个方面。为了研究基于构词法的深度学习在中文文本复杂性分析中的应用，首先探讨了构词法的内涵及其在中文文本分析中的作用。其次，从语言分析中词向量ORE的角度出发，实现了基于词向量的ORE模型。此外，还提出了一种基于词向量距离的提取方法。测试结果表明，在公开的 WEB-500 和 NYT-500 数据集上，所提算法的 F1 值为 67%，优于其他同类文本提取算法。当召回率超过 30% 时，所提方法的准确率高于其他几种最新的语言分析系统。这表明基于 DL 算法和构造语法理论的中文文本提取系统在复杂性分析方面具有优势，可以为中文文本分析提供一种新的研究思路。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.