{"title":"哈萨克语文本语义分析神经计算机系统","authors":"Akerke Akanova, Aisulu Ismailova, Zhanar Oralbekova, Zhanat Kenzhebayeva, Galiya Anarbekova","doi":"10.1145/3652159","DOIUrl":null,"url":null,"abstract":"<p>The purpose of the study is to solve an extreme mathematical problem – semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the LDA model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the LDA model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language\",\"authors\":\"Akerke Akanova, Aisulu Ismailova, Zhanar Oralbekova, Zhanat Kenzhebayeva, Galiya Anarbekova\",\"doi\":\"10.1145/3652159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The purpose of the study is to solve an extreme mathematical problem – semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the LDA model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the LDA model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.</p>\",\"PeriodicalId\":54312,\"journal\":{\"name\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3652159\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3652159","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
本研究的目的是解决一个极端数学问题--自然语言的语义分析，它可用于市场研究、在线翻译和搜索引擎等多个领域。在训练神经网络时，使用了基于 LDA 模型和词的向量表示的数据训练方法。本研究以机器学习和 LDA 模型为基础，介绍了用于哈萨克语文本语义分析的神经计算机系统的开发情况。在研究过程中，就文本识别算法考虑了系统开发的各个阶段。使用 Python 编程语言作为工具，使用大大简化神经网络创建过程的库，包括 Keras 库。为了测试系统的有效性，在专家的参与下进行了一次实验，实验结果证实了系统所提供数据的可靠性。现代计算机语言学家使用各种技术和方法处理自然语言处理问题的论文也在考虑之列。
Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language
The purpose of the study is to solve an extreme mathematical problem – semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the LDA model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the LDA model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.
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.
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