Natural Language Processing (NLP)

 

Natural Language Processing: All You Need to Know

 

Human language is pretty complex. How we write or speak contains many nuances other than just deriving the meaning of the words. We have to understand the sentiment, emotion, and usages like metaphors or sarcasm.

 

Now, this task sometimes turns out challenging even for humans. So, machines or computers find it extra difficult to work with or even understand human speech or text.

 

Here comes the need for Natural Language Processing or NLP. It allows machines to deal with text and speech for a number of purposes and business applications.

 

Let's explore more on the topic.

 

What is Natural Language Processing?

 

 

NLP is a field of computer science and artificial intelligence (AI). It allows computers or applications to understand and interpret the meaning of texts and human speech. Therefore, machines can also give back feedback or answers based on the input text or speech.

 

Apart from AI, NLP relies on computational linguistics to function. Linguistics is the field of working with language and is taught in many colleges across the world.

 

Computational linguistics is the rule-based modeling of our language. Additionally, NLP uses machine learning, deep learning models, and statistics to understand and manipulate human language.

 

Moreover, applications can gauge the sentiment or intent of the speech or text.

 

Natural Language Processing Use Cases

 

Many of us have used NLP, maybe without even realizing it. For example, we are relying on NLP when we ask Alexa to play a song or Google Assistant to perform a search. Additionally, NLP is useful for processing or analyzing large volumes of text or speech.

 

Below are some uses cases of NLP in the real world:

 

Speech to Text Converters

 

Computer applications can convert speech to text rapidly. This is the most simple example of NLP at work. These apps can turn human speech from an audio file into the text of your desired language.

 

Voice-Driven GPS

 

GPS maps are becoming common as we turn to mobile apps for assistance with navigation. These apps can give voice directions to help you reach your distance in the shortest time. You may also give voice commands to find alternate routes or avoid traffic congestion.

 

Website Chatbots

 

Many websites have chatbots driven by NLP and AI. These chatbots are computer programs that can converse with you via text, just like a human. They can respond to your questions about products or link you to resources or a knowledge base.

 

Chatbots are a great innovation as businesses don't have to employ a human to engage website visitors.

 

Virtual Assistants

 

Virtual assistants offered by platforms like Windows, Mac, Android, or iOS work based on NLP. They can process voice commands or speech in real-time to perform desired actions. These virtual assistants can also learn personal preferences over time and provide more personalized recommendations or relevant information.

 

How Does Natural Language Processing Work?

 

NLP relies on a number of techniques to process human language and perform desired tasks. Data scientists or businesses generally use NLP tools to achieve their objectives. These tools convert speech or text into a format that computers understand.

 

Some of the ways NLP uses to gauge human language are:

 

●      How words are created and their relation with associated words

●      How a sentence is formed with words

●      How grammar and lexical meaning influences the meaning of sentences

●      Putting words in context

 

Now, you can use different NLP techniques to perform your desired action. A few examples of NLP methods include:

 

Speech Recognition

 

Speech recognition is the first step to converting human speech into text or computer-readable formats. A virtual assistant can only work when it can recognize and understand what we are speaking.

 

This can be a challenging task as people have accents, speak too quickly, and so on.

 

Text Vectorization

 

Text vectorization is the process of converting text into formats computers or apps can understand. Next, operators feed training data into NLP tools to facilitate machine learning and generate tags. This allows apps to relate a specific input with a specific output.

 

Computers can also use statistical analysis techniques to create knowledge banks. These apps work more accurately as they receive more NLP algorithms.

 

Sentiment Analysis

 

NLP tools can analyze the sentiment of the speaker or text. They can learn to discern emotions, suspicion, opinion polarity, and more. The process is automatic, and there is no need to define any manual rule.

 

Tokenization

 

Tokenization helps computers break down a huge chunk of text or speech into tokens. It can be used for both words and sentences to create smaller and organized semantic units. They make the natural language more accessible to machines by removing elements like punctuation.

 

However, in some cases, such elements may be necessary for accurate analysis or summarization, like in academic papers.

 

Part of Speech Tagging

 

This technique allows machines to tag parts of speech or words grammatically. The process helps apps identify verbs, nouns, adjectives, and more. Machines rely on morphology to facilitate the workflow to classify speech or text.

 

Computers must perform this step accurately to perform the desired action. For example, "bark" can mean a tree's outer layer or a dog's call.

 

Natural Language Processing Tools

 

Programmers may use a range of tools to create NLP apps. One of the most popular among them is Python, which comes with the Natural Language Toolkit (NLTK). It contains ample resources and libraries to create reliable NLP apps.

 

NLTK can implement different NLP techniques, including the ones discussed above. The list includes tokenization, semantic reasoning, and more.

 

Moreover, these Python apps can generate logical deductions from information derived from speech or text.

 

This is what a Python NLP code may look for performing sentence tokenization:

 

 

Final Thoughts

 

NLP is one of the foundations that drive modern technology. From chatbots to spam detection, NLP plays an important role in our professional and daily lives. Data scientists or businesses use different NLP tools and apps to serve their purposes, like Python NLTK. These tools allow you to create NLP apps for business and enterprise applications.