Extracting Information from Unstructured Text with NLP – (6 Ways)

There is so much data, especially unstructured text data, in the world that it’s impossible for humans to manually collect, organize, and study the data to extract actionable business insights. As more and more text data is added to the data pool every day, studying it becomes all the more difficult.

This is where the use of Artificial Intelligence (AI), and, in particular, a type of AI called Natural Language Processing (NLP) comes into the picture for extracting information from unstructured text much in the same way human beings can.

In this article, we discuss the following topics:

• Understanding structured and unstructured data
• Six NLP techniques used for extracting information from unstructured text
• Benefits of NLP for small and large companies
• How Accern can help you

Let’s begin!

NLP in Action in Day-to-Day Activities

You can see NLP in action in many forms in your day-to-day activities on the internet:

• When you search for a term on Google, it uses NLP to fetch relevant results. It will also try to understand the context of your search and show you popular searches close to your search phrase.
• Both Siri from Apple and the Alexa from Google use NLP to extract information. They use voice recognition to understand common phrases. Accordingly, they search their databases to deliver the best results.
• When you visit a website, you might see a chatbot. This chatbot uses NLP to understand your question and to return the closest answer that matches your question.

As you can see, there are several uses of NLP – from asking Alexa to add a product to your shopping cart to translating one language to another. It can take human input and reorganize it in a way that can be parsed by the software.

According to Statista research, the Natural Language Processing market will grow to more than 43 Billion by 2025.

Understanding Structured vs. Unstructured Data

Structured data has a predefined format like columns in a spreadsheet where each field holds a particular type of information. Unstructured data, on the other hand, comprises of different types of data stored in a variety of native formats, such as text documents, surveys, call transcripts, blogs, social media, images, audio, and video files.

Structured data is typically stored in relational databases, whereas unstructured data is often stored in document or content management systems and NoSQL databases. Structured data needs less storage space than unstructured.

There is also a third variant called semi-structured data. It has unstructured data in it but also has metadata that helps identify some characteristics. This metadata enables the data to be cataloged more effectively.

Extracting information from unstructured data sources can be difficult. However, with the right NLP techniques, it can be done easily.

6 NLP Techniques for Extracting Information from Unstructured Text

Here are the most common NLP unstructured text analysis techniques used in extracting information from unstructured text.

1. Sentiment Analysis

Sentiment analysis (also known as opinion mining) is an NLP text extraction technique to find out the tone of the given data. It finds out whether the data has a positive, negative, or neutral tone. It’s often used to analyze customer feedback to find out if they’re happy or not.

There are three different types of sentiment analysis: emotion detection, graded analysis, and multilingual analysis.

Emotion detection

This type of sentiment analysis goes beyond good / bad or positive / negative polarity and detects emotions such as frustration, anger, happiness, sadness, etc. There are certain lexicons that help algorithms detect human emotions.

However, lexicons cannot always be accurate in information extraction from text because different people express emotions in different ways. For example, words like “bad” can be used in negative sentences (“this product is bad”) and also in positive ways (“this product is bad ass”).

Graded analysis

Instead of having just three polarities of negative, positive, and neutral, you can expand the polarity of sentiment analysis for higher precision. Some polarities can be very positive, positive, neutral, negative, and very negative.

This is a more fine-grained sentiment analysis and can be translated to the 5-star rating system where very positive is 5 stars and very negative is 1 star.

Multilingual analysis

Multilingual analysis is an information extraction NLP method that can detect the language in text and then apply sentiment analysis to find a positive, negative, or neutral tone.

2. Named Entity Recognition

Named Entity Recognition (NER) is a Machine Learning (ML) technique that detects certain identifiers and how they are classified according to predefined categories.

For example, let’s consider this text:

Summer Hirst went to Ravensbourne University in 2010 and met Emily Victor there.

NER can recognize Summer Hirst as a person, Ravensbourne University as a university, 2010 as a date, and Emily Victor as another person.

This needs intensive labeling to understand separate words and the categories they belong to. Apart from labeling, the model also needs to understand the context to remove ambiguity. Once the ambiguity is removed, it can be used for extracting information from unstructured text.

NER can be used in several ways. For example, it can be used to train chatbots in banking applications to chat with customers. It can also be used in the medical industry to identify vital terms used in medical reports. Apart from that, it can also be used to read customer reviews to see how many times a specific term is repeated to understand the pain points.

It has several other uses as well. Automating repetitive customer support tasks like the categorization of customer issues can save valuable company time. Customer concerns are automatically sent to the right department where they can be resolved, leading to better customer satisfaction.

3. Topic Modeling

Topic modeling is a ML technique that can scan documents, find phrases and words, and automatically assign certain topics to them accordingly.

It is unsupervised and doesn’t need pre-existing tags given by humans. The topic modeling algorithm extracts some attributes from a large set of words. The most repeated ones are called topics. This classifies the data accordingly, and there is no need to waste time going through all the documents.

With the help of a topic model group, comparable feedback can be grouped. This is done by recognizing certain patterns such as word/phrase frequency and the number of words between two words. These pieces of information can help the algorithm infer the given data for extracting information from unstructured text using algorithms. There are several algorithms or methods for that:

The most common method for topic modeling is Latent Dirichlet Allocation (LDA). According to this algorithm, documents are made of specific topics or tokens. LDA aims to detect topics to which a particular document belongs.

For example, the algorithm creates five topics based on the words in documents. It checks how many times those words (or words related to them) appear in a particular document and thus assigns documents to their topics.

It is kind of like assigning books to a particular shelf of the library. Depending on the contents of the book, the librarian will decide which shelf it will go to.

4. Summarization

NLP summarization studies ways to program computers so they can process huge amounts of data in natural language. Its goal is to reduce the number of words or sentences in a document without altering its meaning.

This can be done in two ways: Extractive and Abstractive.

• The Extractive Model selects the most important words and phrases in the NLP text. It doesn’t necessarily understand the meaning of those terms. This method uses traditional and simple algorithms to summarize the given text. For example, it can work on the frequency of certain words. Based on high-frequency words, it determines which sentences should be present in the final summary.
• The Abstractive Model uses advanced machine learning to understand the meaning or semantics of the text to extract information and creates a meaningful summary accordingly. As evident, abstractive models are more difficult to implement but generate more accurate results.

There are several benefits of summarization:

• Saves time and allows people to extract useful information quickly.
• Increases productivity by enabling users to scan through large texts very fast.
• Ensures all facts are covered with the help of deep learning. Manual skimming through documents can miss some important details.

5. Text Classification

Text classification is also called text categorization or text tagging. It is used to analyze unstructured data. It assigns some tags to the text depending on its content. The three main methods for text classification are rule-based, machine-based, and hybrid.

• In the rule-based method, the model uses a set of linguistic rules to separate text into groups. These linguistic rules are defined and categorized by users. For example, words like Liz Truss and Joe Biden would be in the Politics category.
• In the machine-based method, classifications are based on earlier learnings. For example, let’s say this model is applied to movie reviews. Based on the past reviews, it already has a bag of data words. These words could be “funny,” “comedy,” “tragic,” “sad,” “action,” “boring,” “thrilling,” “fell asleep” and others. The model will find the occurrence and frequency of these words in a given review. Accordingly, it will classify the review.
• The hybrid approach combines rule-based and machine-based approaches. It uses a rule-based method to create tags. And machine learning to classify data based on those tags. It may also use humans to improve the list, which makes it the best text classification method.

6. Dependency Graph

A dependency graph is a data structure that represents how one element of the system interacts with the other elements. It’s made as a directed graph where every node directs to the one it depends on.

A dependency graph uncovers links between neighbouring words. This, in turn, helps in analyzing the grammatical structure of a sentence. It divides a sentence into several sections based on the links between words. Dependency parsing is based on the assumption that there is a relationship between every linguistic unit in a sentence.

When text is arranged as a directed graph, it becomes easier for extracting information from unstructured text.

NLP Isn’t Just for Big Enterprises

NLP sounds enticing but can too complicated. So complicated that one might assume it’s only for big companies with teams of data scientists. However, NLP AI tools like the Accern NoCodeNLP Platform bring powerful AI solutions to small companies as well. You don’t need to have deep coding language or data science experience to let your business gain from AI.

If you’re a less technical user looking forward to deploying AI or NLP solutions without going into coding, Accern can be very helpful for your company as it boosts innovation by customizing workflows.

How Accern Can Help You

If you’re looking for a fast and accurate AI solution, Accern can help empower your business without the need for coding or data science experience.

Accern reduces project delays. You can just click to select the most popular NLP workflows already assembled within our platform.

The Accern NLP AI enhances predictive models by quantifying or structuring unstructured data. You can have complete control over your AI workflow by selecting built-in, or your own data sources and data sinks or dashboards. The Accern NoCodeNLP Platform can reduce risks by quickly testing workflows and discovering which ones work for you.

Accern NLP unstructured text analysis doesn’t need huge volumes of training data. You can start simply by adding keywords, and it does the rest.

Schedule a demo to learn how your business can take advantage of No-Code NLP in extracting infromation from unstructured data!

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