Extracting Structured Data from Text in Natural Language

Q3 Computer Science International Journal of Intelligent Information and Database Systems Pub Date : 2021-08-31 DOI:10.11648/J.IJIIS.20211004.16

Zheni Mincheva, Nikola Vasilev, Ventsislav Nikolov, A. Antonov

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Abstract

Nowadays, the amount of information in the web is tremendous. Big part of it is presented as articles, descriptions, posts and comments i.e. free text in natural language and it is really hard to make use of it while it is in this format. Whereas, in the structured form it could be used for a lot of purposes. So, the main idea that this paper proposes is an approach for extracting data which is given as a free text in natural language into a structured data for example table. The structured information is easy to search and analyze. The structured data is quantitative, while the unstructured data is qualitative. Overall such tool that enables conversion of a text into a structured data will not only provide automatic mechanism for data extraction but will also save a lot of resources for processing and storing of the extracted data. The data extraction from text will also provide automation of the process of extracting useful insights from data that is usually processed by people. The efficiency of the process as well as its accuracy will increase and the probability of human error will be minimized. The amount of the processed data will no longer be limited by the human resources.

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从自然语言文本中提取结构化数据

如今，网络上的信息量是巨大的。它的很大一部分以文章、描述、帖子和评论的形式呈现，即自然语言的自由文本，在这种格式下很难使用它。然而，在结构化形式下，它可以用于很多目的。因此，本文提出的主要思想是一种将以自然语言形式给出的自由文本数据提取到结构化数据(如表)中的方法。结构化的信息便于搜索和分析。结构化数据是定量的，而非结构化数据是定性的。总的来说，这种能够将文本转换为结构化数据的工具不仅为数据提取提供了自动机制，而且还为处理和存储提取的数据节省了大量资源。从文本中提取数据还将为从通常由人工处理的数据中提取有用见解的过程提供自动化。该过程的效率及其准确性将提高，人为错误的可能性将最小化。处理的数据量将不再受人力资源的限制。

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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来源期刊

International Journal of Intelligent Information and Database Systems Computer Science-Information Systems

CiteScore

2.90

自引率

0.00%

发文量

期刊介绍： Intelligent information systems and intelligent database systems are a very dynamically developing field in computer sciences. IJIIDS provides a medium for exchanging scientific research and technological achievements accomplished by the international community. It focuses on research in applications of advanced intelligent technologies for data storing and processing in a wide-ranging context. The issues addressed by IJIIDS involve solutions of real-life problems, in which it is necessary to apply intelligent technologies for achieving effective results. The emphasis of the reported work is on new and original research and technological developments rather than reports on the application of existing technology to different sets of data.