What is NLP in Machine Learning?

What is NLP?

NLP stands for Natural Language Processing (NLP) is a machine learning tech that gives computers the ability to understand human language.

NLP enables machines to understand and generate human language in both ways meaningful and useful.

With the increase in the volume of text and voice data generated every day from social media posts to research articles.

NLP is one of the essential tools for extracting valuable insights, summarizing, and extracting various tasks

Why is NLP important?

Natural language processing is very useful in analyzing text and speech data some examples are differences in dialects, slang, and grammatical irregularities as such day-to-day conversations

Most companies use NLP for several automated tasks:

1. Process large documents and analyze and summarize
2. Analyze customer feedback from call center recordings
3. Usage of Chatbots for Automated Customer Service
4. Classify and extract text and much more

NLP Techniques

There are many techniques to enable computers to process and understand human language which can be classified into several broad areas

1. Text processing contains further different techniques

• Tokenization: As the word suggests words or sentences are split into smaller text
• Stemming and Lemmatization: Reducing the word to its base or root forms
• Stopword removal: Removing filler words such as “and”, “the”,”is” etc which may not carry much significant meaning
• Normalization: Standardizing text like removing punctuation and correcting spelling errors.

2. Syntax and parsing in NLP

• POS – Part of Speech Tagging: Assigning parts of speech to each word in a sentence (ex: noun, verb, adjective)
• Depending Parsing: Analyzing the grammatical structure of a sentence to identify relationships between words
• Constituency Parsing: Breaking down a sentence into its constituent parts or phrases

3. Semantic Analysis

• Named Entity Recognition (NER): Identifying and classifying entities in text like names of people, organizations, locations, dates, etc using the GPTs such as Google Vertex we can use the existing API to enable this feature to extract the NER from the images and categorize it.
• Word Sense Disambiguation (WSD): Determining which meaning of a word is used in a given context. This is most likely relatable to LSTM where it tends to remember the Long and short term memory

Applications of Natural Language Processing:

• Spam Filters
• Algorithmic Trading
• Questions Answering
• Summarizing Information

Summary

The field of NLP which is Natural Language Processing has significantly transformed the way humans interact with machines. Which currently has enabled humans to interact with the machine as a common human starting from text-to-text and now audio-to-text, text-to-audio, and audio-to-audio.

It’s suggested to understand the core concepts of NLP which would help in understanding the capabilities in the modern digital landscape. As “Sundar Pichai” recently stated in the Google I/O 2024 to focus on the basic architectures of GPT to understand how APIs work which would help us to develop applications for tomorrow’s world