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Table of contents
1.
Introduction
2.
Understanding the extracted Information
3.
Frequently Asked Questions
4.
Conclusion
Last Updated: Mar 27, 2024
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Understanding the extracted Information

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Introduction

In the real world, the data can be seen in two different categories. You can learn about how big data works through this link. They are structured and unstructured data. Structured data are like table formats, text, etc. At the same time, Unstructured data includes images, audio, video, documents, emails, customer correspondence, tweets, and other formats. In these current technology developing days, the amount of variety of data is growing largely. Analyzing these types of data needs so many concepts and tools. Among them, one of the major types of data is text. Text is one of the common types of data that is used in our daily life. Understanding how to deal with this type of data is very important. To do this, many analytic techniques have been developed, called text analytical tools. After undergoing several data analysis techniques, we will finally get the insights called information. This output is called the extracted information. We will learn more about this extracted information, how looks, etc.

Understanding the extracted Information

After applying so many data analysis techniques such as lexical/morphological analysis, syntactic analysis, semantic analysis, and discourse-level analysis, we will get final outputs or insights as a form of information. This formatted information is used for fetching useful insights to develop our business. The information after applying some tagging and markup, we will get the following kinds of information.

  • Terms:

Also called Keywords, they are used for querying useful information.

  • Entities:

Usually called “Named Entities”. Defined as specific examples of abstraction.
Example: John Doe can be considered a name of a person. Here Name of a person is called Entity. And Similarly, March o3, 2022, can be considered a Date. Here date is considered as a Named Entity.

  • Facts/Relationships:

Tells how two words are related and majorly indicates what/who/where relationships between two named entities.
Example: John Doe is an employee of the company ABC. Here asking who is the employee of ABC results John Doe, etc.

  • Events

Events describe the state of action, usually contain a time dimension, and often cause facts to change.
Example: Change in management within a company, etc.

  • Concepts:

Concepts are considered as ideas or thoughts. These are described by a set of words and sentences that indicate a specific concept.
Example: The concept “unhappy” includes a set of words such as angry, disappointed, not satisfied, didn’t get a callback, confused, time waste, etc.

  • Sentiments

Sentiments are used to find the feeling or emotions that are underlying the text. Sentiment analysis is a huge concept that can be implemented using the concept of machine learning and deep learning concepts.
Example: Happy, Bad, Not Good, etc.

And not but not least, Taxonomies, Taxonomies is a way of organizing information into a hierarchical fashion/relationship. It majorly deals with how the information is categorized, etc. These taxonomies also play a very important role in natural language processing, especially in the case of Recommendation Systems. Taxonomy actually uses synonyms and alternate expressions in order to organize the information into categories.

Reference -  Big Data For Dummies, A Wiley Brand.

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Frequently Asked Questions

  1. What is meant by information?
    Information is nothing but a  meaningful collection of data, whereas the data can be described as a collection of raw facts. Information can be obtained from data processing. This information can be used to develop insights that grow one’s business.
  2. What is the difference between facts and events?
    Facts are the relationship between two named entities whereas the events are similar to facts but the major difference is that events contain time as a dimension. They cause facts to change from their current state.
  3. What is the format of the information after extracted?
    Information is extracted from data after undergoing several data processing and data analyzing techniques. This information is mainly structured and clean. And also, taxonomies are used to organize the extracted information into useful categories,
  4. How can information be used to develop an organization?
    Information that is extracted from the organization’s data contains useful facts and figures about the organization. They add meaningful information to their current rules and regulations and thus by adding extra information.

Conclusion

In this article, we have extensively discussed the concept of information, how to understand the extracted information, how the extracted information can be organized by using taxonomies, etc. We will explore more about this concept in the upcoming articles.
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