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.
| Published in | International Journal of Intelligent Information Systems (Volume 10, Issue 4) |
| DOI | 10.11648/j.ijiis.20211004.16 |
| Page(s) | 74-80 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Data Extraction, Structured Data, Unstructured Data, Automation, NLP, RASA
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APA Style
Zheni Mincheva, Nikola Vasilev, Ventsislav Nikolov, Anatoliy Antonov. (2021). Extracting Structured Data from Text in Natural Language. International Journal of Intelligent Information Systems, 10(4), 74-80. https://doi.org/10.11648/j.ijiis.20211004.16
ACS Style
Zheni Mincheva; Nikola Vasilev; Ventsislav Nikolov; Anatoliy Antonov. Extracting Structured Data from Text in Natural Language. Int. J. Intell. Inf. Syst. 2021, 10(4), 74-80. doi: 10.11648/j.ijiis.20211004.16
AMA Style
Zheni Mincheva, Nikola Vasilev, Ventsislav Nikolov, Anatoliy Antonov. Extracting Structured Data from Text in Natural Language. Int J Intell Inf Syst. 2021;10(4):74-80. doi: 10.11648/j.ijiis.20211004.16
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Download@article{10.11648/j.ijiis.20211004.16,
author = {Zheni Mincheva and Nikola Vasilev and Ventsislav Nikolov and Anatoliy Antonov},
title = {Extracting Structured Data from Text in Natural Language},
journal = {International Journal of Intelligent Information Systems},
volume = {10},
number = {4},
pages = {74-80},
doi = {10.11648/j.ijiis.20211004.16},
url = {https://doi.org/10.11648/j.ijiis.20211004.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211004.16},
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.},
year = {2021}
}
TY - JOUR T1 - Extracting Structured Data from Text in Natural Language AU - Zheni Mincheva AU - Nikola Vasilev AU - Ventsislav Nikolov AU - Anatoliy Antonov Y1 - 2021/08/31 PY - 2021 N1 - https://doi.org/10.11648/j.ijiis.20211004.16 DO - 10.11648/j.ijiis.20211004.16 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 74 EP - 80 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20211004.16 AB - 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. VL - 10 IS - 4 ER -
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