What Is Information Extraction & How to Automate It in 2023?

Learn more about the definition, benefits, and methods of information extraction, and how you can automate it for higher efficien

 min. read
May 28, 2024
What Is Information Extraction & How to Automate It in 2023?

In the landscape of modern data analysis and decision-making, the ability to sift through vast volumes of unstructured text and distill relevant information is of paramount importance. But worry no more, as information extraction can simplify everything. By leveraging automated techniques, it empowers you to convert raw texts into structured data.

From finance to healthcare and even e-commerce, the impact of information extraction spans across sectors, revolutionizing how businesses process and utilize information. Through this article, we’ll uncover the transformative power of the information extraction. We’ll also discuss its role in shaping the future of data-based decision-making. Let's delve in!

Information Extraction (IE) is a computational process of automatically extracting structured and meaningful information from unstructured or semi-structured text sources. Unstructured text data includes sources like articles, reports, emails, social media posts, and more, where information is not neatly organized into columns and rows as in structured databases. IE aims to transform this unorganized text into a systemized format that can be used for various applications.

Remember, at its core, IE goes beyond simple keyword-based searching or pattern matching. It involves identifying and extracting specific pieces of information. These are - entities (e.g., names of people, organizations, locations), relationships between entities, events, and their attributes. The collected structured data makes information more accessible to businesses and helps in data-driven decision-making.

Example of Information Extraction

Let’s say you have thousands of reviews for your e-commerce business. You want to pull out information about these reviews from the available data. With information extraction, you can instantly get details about;

  • Names
  • Ratings
  • Products
  • Liked and disliked features etc

Now, based on extracted data, you can easily decide which products and features need improvements.

Some of the techniques you can use for information extraction are:

In this method, experts establish rules to extract particular pieces of information from unstructured data. It mainly goes through three processes;

  • Rule Creation: At this stage, experts define the rules or patterns or set keywords.
  • Pattern Matching: It will find information based on the rule and indicates wherever the desired pattern is matched.
  • Extraction: Once the pattern is matched, now the system will extract the information systematically for more analysis.

Drawback

The main drawback of rule-based extraction is limited flexibility. It follows predefined rules, which makes it hard to accommodate changes. On top of that, when rules overlap, the outcome is usually inaccurate. Moreover, due to extreme dependency on rules, the quality of the result entirely depends upon the rule definition.

It’s all about training models directly from the data and then making decisions accordingly. It works by;

  • Feature Selection: Initially, create the feature which ML models can use to make predictions.
  • Input: Now, provide a labeled dataset to train models for later use.
  • Model Training: Now, using algorithm help models learn the patterns.
  • Outcome: Finally, based on learning and training, models will analyze the texts and predicts missing pieces automatically.

Drawback

The biggest drawback of ML-based extraction is a dependency on high-quality training data sets. If training data has biases, the model will also generate biased outcomes. On top of that, some outcomes might make you question why a particular result was generated.

NLP basically extracts structured information from unorganized data using various natural language processing techniques. Some of the NLP techniques include;

Text Summarization

Text Summarization is a method used to condense lengthy pieces of text into shorter versions while retaining the essential information and main ideas. There’re two primary approaches to text summarization: extractive and abstractive.

  • Extractive Summarization: In this approach, sentences or phrases are selected directly from the original text to form the summary. It involves identifying the most critical sentences using algorithms considering factors like sentence relevance, frequency, and importance.
  • Abstractive Summarization: Abstractive summarization involves generating new sentences that properly capture the core meaning of the original text. It goes beyond mere extraction and can rephrase and combine sentences to create concise and coherent summaries.

Named Entity Recognition (NER)

It's a technique used to find and classify entities such as names of people, locations, companies, dates, and more within a given text. NER algorithms analyze the text to identify patterns and contexts that indicate the presence of these entities.

It's the most time-consuming technique and is used in various applications, from information retrieval to question-answering systems. For instance, in a purchase order, NER can identify the quantity of goods, buyer and seller names, and prices of products and goods, providing structured information for further analysis.

Tokenization

Breaking down a text into smaller units/chunks called tokens. They are typically words or subwords and the first step in natural language processing (NLP) and information extraction. Tokenization helps in preparing text for analysis by dividing it into manageable chunks. Some examples include:

  • One-Word Tokens: [The, quick, brown, fox, jumps, over, the, lazy, dog.]
  • Two-Word Phrases (Bigrams): [The quick, quick brown, brown fox, fox jumps, jumps over, over the, the lazy, lazy dog, dog.]
  • Three-Word Sentence: [The quick brown, quick brown fox, brown fox jumps, fox jumps over, jumps over the, over the lazy, the lazy dog.]

Parts of Speech Tagging

Parts of Speech (POS) tagging in NLP involves assigning specific grammatical categories or "tags'' to each word in a sentence. POS does everything from providing information about the syntactic role and grammatical function to contextual relationships of words within the sentence.

Sentiment Analysis

It’s an NLP information extraction technique that determines the emotional tone or sentiments expressed in a text. Also known as opinion mining, it aims to identify whether a given text expresses positive, negative, or neutral sentiments. It’s widely used to gauge public opinions, understand and comprehend customer feedback, and make informed decisions based on textual data.

Dependency Graph

Another technique that supports the extraction of information is Dependency Graph. It's a visual representation that illustrates the relationships between elements in a system. It captures how words are connected based on their syntactic roles, such as subject, object, predicate, modifier, and more. Dependency graphs help reveal the underlying structure of a sentence and how its components interact.

Some other benefits of IE using advanced techniques include:

High Accuracy

Information extraction employs advanced algorithms and techniques to analyze text, ensuring that extracted information is highly accurate. Unlike manual methods, which lead to errors due to human oversight or misinterpretation, automated extraction systems either follow predefined rules and patterns or leverage AI technologies to learn from samples and past data extraction tasks. This precision is especially critical in industries where accurate data forms the foundation of decision-making, such as finance or healthcare.

Boosts Employee Productivity

Manual extraction of information from text sources is time-consuming and requires careful attention to detail. Automating this process frees employees from the mundane and repetitive task of manually extracting data. This results in a notable increase in productivity as employees can now devote their time and expertise to higher-value activities.

Less Operational Costs

The shift from manual extraction to automated information extraction systems brings about significant cost savings. Manual data entry often involves hiring and training personnel solely for data-related tasks, which can be resource-intensive.

Furthermore, manual information extraction is inefficient and requires additional time and effort for correction. Automation significantly reduces the labor costs associated with manual data entry and mitigates the potential for errors, thereby minimizing the need for costly rectifications.

Quick Turnaround Time

The business world is changing, and the speed at which information is processed and utilized can make a significant difference. Automated information extraction systems operate swiftly, drastically reducing the time it takes to transform unstructured data into valuable insights.

Tasks that would have consumed hours or even days using manual methods can be completed in a fraction of the time. This faster turnaround time enables timely decision-making, allowing businesses to respond promptly and accurately to market changes, customer needs, and emerging trends.

Looking for a reliable and powerful information extraction tool? FormX is here to help!

FormX is an innovative information extraction tool that transforms physical documents into structured digital data. Our cutting-edge technology caters to various document types - invoices, receipts, purchase orders, bank statements, shipping orders, and more. With FormX, you can effortlessly extract critical information from these documents, streamlining your data processing workflow. What sets FormX apart is its commitment to user convenience and efficiency. We've designed the platform to be a game-changer for businesses and developers.

The future of information extraction is poised to be transformative, driven by advancements in artificial intelligence, machine learning, natural language processing, and large language models like GPT-4. As these technologies continue to evolve, information extraction is expected to reach unprecedented accuracy, efficiency, and adaptability levels.

Integrating more sophisticated algorithms, such as deep learning models and contextual embeddings, promises to enhance the extraction of complex relationships and nuanced insights from unstructured text. With the proliferation of IoT devices, the future may also witness the inclusion of real-time data extraction from various sources.

In conclusion, informative extraction is helpful for businesses and other educational facilities. It helps you find meaningful data from unstructured text, easing your decision-making process. If you are searching for the best information extraction tool for automating the whole data extraction process, FormX can be the most desirable one.

This tool revolutionizes the way you identify critical information from a sea of unstructured data. Even novices can use it thanks to its smooth and easy-to-navigate user interface. So, even without experience with such tools, you can easily use FormX. Contact us now to streamline your information extraction processes with accuracy and reliability.