Natural language processing (or NLP) is a branch of artificial intelligence that focuses on giving computers the ability to understand text and spoken words, similar to how humans do.

It combines computational linguistics (a branch concerned with how human language is processed and interpreted) with statistics, machine learning, and deep learning models. Together, they enable computers to process human language, whether text or voice and understand its meaning and the intent of the writer or speaker.

Natural language processing allows computers to:

• Translate text from one language to another
• Respond to textual or spoken commands
• Summarize large volumes of text

To breakdown spoken or textual data in a way that allows computers to have the ability to understand it and take the appropriate course of action, the following occurs:

Speech Recognition:

• Also known as speech-to-text, it converts voice data into textual data.
• Every application that accepts voice commands requires this function.
• The challenge lies in the large variety of ways people speak. It includes fast-talking, mumbling words, having an accent, using incorrect grammar, or using a different language altogether.

Speech Tagging:

• Also known as grammatical tagging, it involves reading a sentence and classifying parts of the speech as nouns, verbs, adjectives, and so on.

Word Sense Disambiguation:

• It involves determining the correct meaning of words having multiple definitions through analysis and identifying what makes the most sense in the given context.

Named Entity Recognition:

• It involves classifying words or phrases that refer to entities.
• For instance, “Gurgaon” is categorized as a place and “Ishaan” is classified as a name.

Co-reference Resolution:

• It involves identifying whether two words are referring to the same entity.
• For instance, in the phrase “Ishaan went to the store. He bought some groceries.”, the Co-reference resolution would identify that “he” refers to “Ishaan” in the sentence.

Sentiment Analysis:

• It involves identifying and extracting sentiments such as attitude, emotion, sarcasm, and confusion from the text.

Natural Language Generation:

• It involves organizing, converting, and structuring information into human language, either as text or voice.

Various applications use natural language processing. Some examples of its use cases are:

Spam Detection:

• Some of the best spam-detecting technologies use natural language processing.
• They scan emails for language patterns that often indicate spam or phishing intentions, such as overuse of financial terms, poor grammar, threatening language, inappropriate urgency, or misspelt company names.

Machine Translation:

• Platforms like Google Translate utilize NLP technology to effectively replace words from one language with words from another while accurately capturing the meaning and tone of the input and maintaining it during translation.

Virtual Agents and Chatbots:

• Virtual agents like Google Assistant, Siri and Alexa use speech recognition to analyze voice commands and natural language generation to respond with the appropriate actions or comments.
• Chatbots use the same techniques while responding to text inputs.

Social Media Sentiment Analysis:

• Social media businesses use natural language processing to discover hidden insights in their data.
• They analyze the language used in posts, comments, and feedback to extract emotions expressed towards products, promotions, and events.
• Companies use this data during product designing, advertising and campaigning.

Text Summarization:

• Large volumes of text use natural language processing techniques to create summaries for indexing purposes, research databases, and for individuals who may not have the time to read the full text.
• A powerful text summarizing software employs semantic reasoning, which finds new facts from existing data based on rules and ontology, along with natural language generation, to add context and conclusions to text summaries.

Natural language processing (or NLP) has revolutionized how computers interact and understand human language, using sophisticated algorithms and models, leading to extensive applications across various industries.

As NLP develops, we can expect more transformative innovations to reshape how we interact with technology.