Information extraction (IE) finds its roots in the early development of natural language processing (NLP) and artificial intelligence (AI), when the focus was still on rule-based systems that relied on hand-crafted linguistic instructions to extract specific information from text. Over time, organizations shifted to techniques like deep learning and recurrent neural networks (RNN) to improve the accuracy of information extraction systems. Today, most NLP applications include information extraction as an important component, and organizations use advanced AI and machine learning (ML) models and frameworks, such as retrieval-augmented generation (RAG), to further the improvements.

In this article, we’ll talk about information extraction with particular emphasis on natural language processing and retrieval-augmented generation.

What is information extraction?

Information extraction is the process of extracting requisite structured data from semi-structured or unstructured text-based data sources, such as PDF documents, web content, AI/large language model (LLM) generated content, etc. 

An example 

Here’s an example demonstrating the kind of data you can expect to pull using an information extraction system:

News article excerpt:

“Apple announced the launch of the iPhone 15 on September 12, 2023. Tim Cook, the CEO, stated that the new phone would feature a faster chip and improved camera technology.”

Information extracted:

• Entity (Organization): Apple
• Entity (Person): Tim Cook (CEO)
• Event (Product Launch): iPhone 15
• Date: September 12, 2023

This example demonstrates key data points extracted from the source (news excerpt). The system has identified two entities, “Apple Inc.” (organization) and “Tim Cook” (person). It also extracted the event “iPhone 15 launch” along with the date “September 12, 2023”. The extracted information can then be used as needed, for example, to update databases or generate summaries or highlights.

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Where does natural language processing (NLP) fit in?

Natural language processing (NLP) is a branch of AI that facilitates interaction between humans and computers, including other machines. Instead of using complex queries or lines of code, you can talk to your systems in simple English and instruct them what to do, including asking for specific information from a data source.

According to Statista’s market insights report, the market size for text-based NLP is set to increase from USD 8.21 billion in 2024 to USD 33.04 billion in 2030. The projected growth highlights significant trends:

• Increasing demand across industries
• Advances in AI models and NLP capabilities
• Rising importance of text-based NLP

Since IE involves extracting structured data from unstructured text, NLP techniques allow machines to analyze and understand human language and process text meaningfully. So, when you can simply say something like “Provide the names of all employees aged over 40”, why resort to something like “SELECT name, age FROM employees WHERE age > 40” to extract the information you need?

NLP plays a foundational role in information extraction. As such, it can enhance, and even replace, several traditional methods of interacting with machines to extract information:

Manual information extraction from text

Reading and analyzing text to pull out requisite information, such as names or dates, from documents or emails without an AI assistant by your side is no longer sustainable, even in the short term. The obsolescence is even more evident in industries like legal and healthcare where timely access to relevant data is critical. AI-powered information extraction tools with built-in NLP capabilities not only automate the process but also deliver accurate information when it’s needed.

Search queries (keyword-driven search)

Traditional search engines rely heavily on exact keyword matches, often producing irrelevant results if the exact keywords aren’t used. With natural language search (NLS) and semantic search capabilities, NLP enables systems to understand the context and intent so that you get relevant results.

Command-line and graphical user interfaces

With a typical command-line interface (CLI) you need specific commands to perform tasks like navigating files or extracting information. Similarly, a graphical user interface (GUI) enables you to interact with computers via icons, buttons, and dropdowns. However, both these methods become cumbersome with complex and large data sets. Using natural language-based question-answering, you simplify these processes to the extent that even business users can work with data.

How does NLP information extraction work?

Extracting information from unstructured text comprises several steps and leverages multiple NLP techniques. While the actual workflow will depend on the type of your document source and the information you need to extract, the overall process is largely the same:

Text preprocessing

Before you extract any data points, you’ll need to clean and break down the source text into its basic components. This happens via tokenization, which, in an NLP pipeline, is a technique to split unstructured data into smaller chunks, or discrete elements, to simplify machine analysis. There are several ways to tokenize source text.

Continuing with the example of iPhone 15 news article excerpt we discussed above, the sentence “Apple announced the launch of the iPhone 15 on September 12, 2023” is tokenized as:

[‘Apple’, ‘announced’, ‘the’, ‘launch’, ‘of’, ‘iPhone’, ‘15’, ‘on’, ‘September’, ‘12’, ‘2023’]

Next, common words like “the” or “of” are removed as part of stop word removal as they are not meaningful and do not carry useful information. To reduce variations of words, they are converted to their root forms, for example “announced” becomes “announce”. This is called lemmatization.

Part-of-speech (POS) tagging

The next step in the NLP information extraction workflow is to assign each token its part-of-speech (POS), i.e., whether a token is a noun, verb, adjective, etc. POS tagging enables the machine to comprehend the grammatical meaning of each word. For example:

Apple (noun), announced (verb), launch (noun), iPhone (noun), 15 (number), September 12, 2023 (date)

Named Entity Recognition (NER)

NER is where the system identifies and classifies important entities based on the context in which they appear in the text by using predefined lists and ML models. For example, from the sentence “Apple announced the iPhone 15 on September 12, 2023,” the NER technique would extract:

• Apple Inc. (ORG)
• iPhone 15 (PROD)
• September 12, 2023 (DATE)

Dependency parsing

Dependency parsing enables the pipeline to identify grammatical relationships between the words in a sentence. Establishing these relationships is important for the system to understand what happened, when, where, by whom, and to whom.

“Apple (subject) announced (verb) the iPhone 15 (object) on September 12, 2023.”

Relation extraction

Now that the system has a fair idea of entities and grammatical relationships, it uses the relation extraction technique to identify relationships between entities. Relation extraction itself relies on a combination of ML models to detect such relationships. An example of relationships between entities could be:

• For the entities iPhone 15 (PROD) and Apple (ORG), the relationship can be defined by “Manufactured-by”, linking iPhone 15 to Apple. This indicates that Apple is responsible for manufacturing the iPhone 15.

Event extraction

For the system to understand and link entities and relationships into a coherent event, it must identify actions and occurrences in the source text. For example, in the sentence “Apple announced the iPhone 15 on September 12, 2023,” the event is the product launch of iPhone 15. So, it identifies the following components and categorizes the event type (product launch):

• Subject (Who): Apple
• Action (What): announced
• Object (What): iPhone 15
• Date (When): September 12, 2023

Template filling

Once the pipeline has extracted all the relevant entities, relationships, and events, it organizes and presents the information in a structured format. In this case, the extracted information will look like:

• Event: Product Launch
• Organization: Apple
• Product: iPhone 15
• Date: September 12, 2023

The role of NLP in intelligent document processing (IDP)

NLP enhances intelligent document processing (IDP) by enabling machines to analyze and comprehend text in documents so that you can derive actionable insights from unstructured data. Key functions of NLP in IDP include:

• Document understanding
• Information extraction
• Document classification
• Data enrichment
• Summarization

Organizations across different sectors use NLP to enhance their document processing capabilities. Here are some notable applications:

Invoice processing

To automatically extract relevant information from invoices, such as vendor names, amounts, and due dates and streamline accounts payable processes.

Contract analysis

To identify key clauses, obligations, and terms in legal documents and enable better compliance and risk management.

Email processing

To extract actionable information from incoming emails.

These functions and applications translate to undeniable business benefits:

Increased efficiency

Automating the information extraction and processing from a variety of documents saves time and reduces manual effort.

Improved accuracy

Advanced NLP techniques, such as NER, OCR, and text classification, enhance the precision of information extraction and the overall data quality.

Scalability

NLP pipelines can handle large volumes of documents at an accelerated pace.

What about retrieval-augmented retrieval (RAG)?

Retrieval-augmented generation (RAG) is an AI framework that combines information retrieval from external knowledge bases or databases with text generation using a large language model (LLM). It’s an approach to improving natural language understanding (NLU) and natural language generation (NLG) tasks, particularly in areas like question-answering and conversational AI.

While NLP primarily focuses on understanding and processing the text within documents, RAG enhances information extraction by incorporating external data sources and providing contextually informed extraction capabilities, including:

• Fact completion by filling in missing information
• Enriching extracted data with additional context for contextual accuracy
• Using external knowledge to correctly detect and link entities

Using RAG for intelligent document processing (IDP)

Using RAG for intelligent document processing (IDP) can aid your organization improve its document handling capabilities. It’s particularly valuable in industries that deal with high document volumes and where accuracy and context are critical, such as finance, legal, and healthcare.

Let’s take an example scenario to understand how you can use RAG to extract information from documents, such as a corporate knowledge base or internal documentation.

Suppose your organization needs to process a large number of invoices to extract key information for financial analysis and reporting.

Input document

An invoice from a supplier contains:

“Invoice Number: INV-12345, Total Amount: $10,000, Due Date: 2024-12-01.”

RAG process

Retrieval:

The RAG pipeline retrieves relevant information from an internal database (e.g., vendor profiles, payment history). For example, it retrieves the vendor’s name “ABC Supplies”, and payment terms associated with the invoice (say, net 30 days).

Generation:

The generative model synthesizes this information, incorporating the retrieved details into the extracted data.

Output

Here’s what your final structured output can look like:

• Invoice Number: INV-12345
• Vendor Name: ABC Supplies
• Total Amount: $10,000
• Due Date: 2024-12-01
• Payment Terms: Net 30 days

RAG-enhanced NLP for intelligent document processing (IDP) 

Traditional NLP is excellent for core IDP tasks: form field extraction, entity extraction, text classification, and sentiment analysis. It works well with structured documents that follow a consistent format like invoices, where there’s less need for deep contextual understanding. RAG-enhanced NLP, on the other hand, combines traditional NLP-based IDP with retrieval mechanisms to extract contextually relevant information from external knowledge bases and sources.

When choosing between traditional NLP and RAG-enhanced NLP for IDP, your decision should take into account:

• Your specific use case
• Processing requirements
• The complexity of the documents
• The outcomes you aim to achieve 

Choose NLP when:

• You need to automate routine document processing tasks with predefined data extraction requirements. 
• You require minimal domain-specific knowledge to understand and categorize document content. 
• Your focus is primarily on structured information extraction and document classification. 
• You have a well-defined set of documents that don’t require extensive contextual understanding.

Choose RAG-enhanced NLP when:

• You require more contextually aware information extraction that considers relationships between data points. 
• Your documents are dynamic, i.e. they vary widely in structure and content, and the information needs to be up-to-date. 
• You are dealing with complex queries that involve generating comprehensive responses based on multiple data sources. 

Whether you choose one or the other, you need a reliable IDP tool to extract information from your documents—and this is where Astera comes in.

Build your intelligent document processing pipeline with Astera Intelligence 

Astera automates the information extraction process from various document types, including invoices, W-2 forms, purchase orders, credit reports, medical documents, shipping documents, and more. 

Here’s how Astera Intelligence helps organizations like yours: 

• Our AI solution learns and adapts to different document formats and creates templates automatically 
• Just specify the fields you need, and our AI will intelligently extract the relevant data across multiple formats 
• Handle EDI and delimited files with both rule-based and AI-driven mapping 
• Search and extract key information from documents across your organization 
• Leverage RAG to conduct smart searches within your documents 
• Our solution integrates seamlessly into your existing document management systems 

Ready to get that last bit of detail out of your documents? Try Astera Intelligence.