Natural Language Processing (NLP): A Comprehensive Guide

As a technological innovation, NLP represents a paradigm shift. It revolutionizes the way we interact with technology and enhances productivity within organizations.

In the following guide, we will tell you what NLP is and how it works so that you can use this wave of innovation to your advantage.

What Is Natural Language Processing?

Natural Language Processing is a machine learning technology that enables computers and other digital devices to understand and generate text and speech. It employs mathematical models that transform human language into machine-readable formats.

While this process may seem straightforward, it’s essential to recognize the subtle complexities inherent in human linguistics compared to computational language's rule-based, structured nature.

Natural Language Processing Models

New technologies allow NLP programs to learn from experience, mimicking human neural networks. For these programs to interpret and generate language, they must first be configured with computations and codes that they can identify and process.

Let's examine the two main models that facilitate this:

Logical Models

In the early stages of NLP, ruled-based and hand-coded approaches prevailed. However, they had limitations due to the inherent nuances of human languages.

Probabilistic Models

Also known as Statistical Models, they combine computer algorithms, Machine Learning (ML), and Deep Learning. These models assign statistical probabilities to NLP processes.

How Does Natural Language Processing Work?

Natural Language Processing works in two main ways: it decomposes human text and voice input into computationally manageable parts, and it converts structured data into natural human language.

To achieve this, several mechanisms take place:

• Grammatical Tagging: this involves identifying the grammatical roles of words according to their use and position in a sentence to help the system interpret its structure and meaning.

• Speech Recognition: it’s a process by which voice data becomes text data, allowing machines to understand and reproduce human speech. To accurately transcribe what is being said, speech recognition should consider different factors such as accents, emphasis, grammatical errors, and intonation.

• Named Entity Recognition (NER): this is used to identify within the text specific entities such as names of people, places, organizations, dates, etc. For instance, in the sentence “Accel One is a company in Seattle” NER would identify a company name and a place.

• Word Sense Disambiguation: in the case of words with two or more meanings, this mechanism would identify the correct one depending on context.

Natural Language Processing Applications

NLP is part of our lives, as it is integrated with several applications that we use every day. Below, we will review just a few of the most common examples and how they work.

Chatbots & Virtual Assistants

Virtual assistants are conversational user interfaces that interact with people through speech recognition and generation, while chatbots specialize in written conversation.

Both recognize the words spoken or written to them, but they also understand their meaning to provide relevant and useful responses based on pre-built dialogues.

Text Extraction

Text extraction consists of withdrawing texts from documents, images, and other sources for data analysis purposes.

A good example of this is OCR (Optical Character Recognition), which scans documents and images, identifying the characters and turning them readable for machines.

Machine Translation

Machine translation uses computer algorithms to translate text or speech from one language to another that goes beyond mere word-for-word translation, considering context, idiomatic expressions, and metaphors.

Online tools and platforms provide accurate, fast translations thanks to large-scale data, neural networks, and powerful hardware, enabling machine translation to improve significantly over the last years.

Sentiment Analysis

Sentiment analysis focuses on getting subjective information from text, such as emotions, opinions, and even sarcasm.

Text Classification

Text classification involves scanning and categorizing text to turn unstructured data into meaningful insights for analysis.

By automatically labeling text with predefined categories, this process helps organize and structure large volumes of information, making it easier to analyze and derive actionable insights.

Real-World Impact Examples: NLP Transforming Industries

Applications for NLP can be found in many different work scenarios. Some of them would include:

• Spam and phishing detection through text classification: text-scanning algorithms can identify message inconsistencies, detect specific keywords, and recognize threatening language.

• Sentiment analysis for marketing and advertising purposes: emotions can be detected from client feedback, online reviews, and social media posts to understand how people feel about a company and use it strategically.

• Text extraction in healthcare: extracting relevant information from clinical records and lab reports.

What Is the Link between NLP and AI?

NLP stands as a branch within artificial intelligence (AI), dedicated to empowering computers to grasp, decipher, and produce human language in manners that hold significance and utility.

As you can see, AI’s impact on software development enhances code generation and streamlines communication between developers and end-users.

Streamline Your Operations and Boost Efficiency with NLP

With a solid understanding, you can integrate NLP into business operations to improve performance and address complex challenges.

If you have additional ideas, reach out and share your project details with us! We are here to support your digital transformation journey, assisting your teams in adopting AI and preparing for future technological advancements.