Each day organizations encounter a vast amount of text as unstructured data like web pages, spreadsheets, and document files. The entities in the text enable people to understand the content of the text. Entity Extraction pulls out the names of people, brands, companies, cities, and countries from a document. It is done using Natural Language Processing which extracts specific data from unstructured text and classifies it under predefined categories. The categories include names, numerical expressions, phone numbers, dates or monetary values. The data in the categories are used for further analysis. It helps people find meaningful information in massive amounts of unstructured text.
Steps involved in extracting an entity
The first step is detecting a word or string of words that form an entity. The second step is creating entity categories like person, organization and location.
Applications of information extraction
Customer support: It helps categorize user requests and complaints and improve response times. Complaints are automatically sent to the relevant department without any human intervention. It helps obtain serial numbers, delivery dates and product types from invoices.
Human resources: It summarizes the applicants’ CVs and speeds up the recruiting process. It enables the identification of the most relevant information about the candidates. It also helps to categorize employees’ complaints and improves internal workflows.
Content classification: It quickly classifies content by identifying the subjects and themes of articles and posts. It helps to customize the content according to customers’ preferences.
Healthcare: It reduces the workload of healthcare professionals by extracting essential information from lab reports and helps improve patient care standards.
Search engines: It improves the speed of search results by summarizing discussions, texts and reviews.
Academia: It allows students to find relevant information faster by highlighting key terms and topics and summarizing the text material.
Information extraction helps to convert unstructured data into structured data. The structured information is machine-readable, which allows the further process of the data.
There is an overwhelming amount of unstructured data, which may contain vast amounts of helpful information. It is essential to identify various entities to structure the unstructured data. Data is beneficial only when available in a structured form. Extracting relevant data manually from a text is challenging. Information extraction tools help to extract required data quickly.
The most significant benefit of structured data is that machine learning algorithms easily use it. Machines easily understand organized information.
Structured data is helpful to businesses. They can gather necessary information about products, brands and customer feedback from structured data. It helps them find ways to provide better products and services to their customers.
More tools are used to analyze structured data. It is challenging to analyze unstructured data. Structured data is generally stored in data warehouses in a predefined format.
Features of information extraction tools
Entity Extraction tools are multilingual and support several popular languages. The tools translate the entities obtained from a foreign language. The information extraction is done accurately at high speed. Many extraction tools integrate easily, and the tools are customized according to the needs of an organization.
Entity extraction is essential to gather valuable and relevant information from unstructured data. Any amount of data becomes useful only when it provides relevant information that helps organizations boost their performance. Extracting data quickly and accurately is essential for the growth of a business or organization.
