Automated document processing can ease the hassle of tedious paperwork. However, the implementation of NLP in intelligent document processing is what enhances its efficiency and effectiveness.
Today, businesses use a large amount of data to make informed decisions and improve their processes and offerings. To ensure utilizing this available data is convenient, enterprises store data in a structured manner. However, a lot of the data available is in an unstructured format. Unstructured data, as the name suggests, is any type of data that does not have a predefined form or structure. This often indicates that the data available needs to be organized in a way that is easily searchable, categorized or analyzed using traditional data management methods. Some unstructured data examples are images, web pages, emails and text documents. To extract data from unstructured formats, businesses are now using NLP in intelligent document processing.
The healthcare and banking industries are just two examples of sectors that require extensive documentation and paperwork to operate efficiently. These industries deal with a large volume of documents, ranging from patient medical records to loan applications and financial statements. Processing these documents manually can be a time-consuming and error-prone task. To address these challenges, many organizations in these industries are turning to automated document processing. By automating document processing, businesses can significantly increase productivity and efficiency while also reducing the risk of errors. This can lead to a better customer experience and higher satisfaction rates. While intelligent document processing is a powerful tool for automating document management and data extraction, it is the integration of natural language processing (NLP) that drives the intelligence behind the automation. NLP consists of the development of algorithms and techniques that enable machines to understand, interpret and generate human language. It encompasses a range of tasks, such as text processing, language translation and speech recognition.
USING NLP IN INTELLIGENT DOCUMENT PROCESSING FOR BETTER RESULTS
Here are some ways NLP can be used for intelligent document processing for businesses to gain remarkable results from their documents.
Text Extraction
Data extraction is made seamless with the assistance of NLP. It can be used to extract text from unstructured documents, such as PDFs and scanned images. This text can then be further analyzed for processing to gain valuable insights.
Information Extraction
Humans often indulge in tedious and repetitive documentation. NLP techniques can identify and extract critical information such as names, dates, addresses and other entities from documents, increasing data accuracy and productivity.
Sentiment Analysis
Sentiment analysis plays a crucial role in helping businesses understand customer sentiments. NLP can be used to identify the sentiment expressed in a document, which can help analyze customer feedback, reviews and social media reactions.
Topic Modeling
Dealing with massive volumes of information can overwhelm the firm’s resources. Natural language processing can automatically identify topics within a document or group of documents, which can be further used for organizing and categorizing large volumes of information.
Language Translation
Whether it’s for internal or external communication, NLP can automatically translate documents from one language to another. This can help in the development of forms for feedback, reviews, or just to collect information that can be easily translated into another language. Organizations with multilingual employees or customers can leverage this method to be more inclusive.
TO SUMMARIZE
NLP in intelligent document processing is a powerful tool that can offer numerous benefits to organizations. By leveraging NLP, organizations can streamline their document processing and operations, which in turn saves valuable time and resources.
