Natural-Language-Processing

Introduction:

Natural Language Processing (NLP) is an emerging technology field, which uses AI to process human language either through text or audio and provide appropriate results.  This blog is mainly for anyone who is working in AI or for those who are fascinated by AI and the technologies encompassing it. 

Natural Language Processing is a sub-field of AI where we enable machines or computers to understand human language through text or audio medium. The machine can process the text or audio provided as input and return a result or processed data based on the prompts provided by the human user. 

It is already implemented in Android apps like Google Assistant or Siri by Apple. 

The amount of data we are collecting globally is growing at an exponential pace. And while the data is piling up, we as human analysts simply can’t keep up with this pace of data generation. 

Natural Language Processing (NLP) stands as a technology, that bridges the gap between human language and AI. This fascinating technology enables machines to understand, interpret, and also generate human language responses. NLP essentially combines computational linguistics with statistical, machine learning, as well as deep learning models. 

In this blog, we’ll travel through the vast landscape of Natural Language Processing(NLP), its core concepts as well as some applications. 

The concept of NLP: 

At its very core, NLP means the interaction between computers and human language. It includes a range of tasks, from simple things like text tokenization to more complex tasks such as sentiment analysis. 

Text Preprocessing: 

Some of the important steps to process the text data before NLP are: 

1. Lowercasing:

As the name suggests, this step mainly involves converting all characters into lowercase, which mainly results in not only a consistent representation of words throughout the text but also, eases the process of tokenization. 

2. Stemming:

This step mainly involves converting related words into a common base form. The main use of this technique is that it minimizes the variations due to tenses, plurals, or conjurations. Also, this step can increase the accuracy of results when using the text in search engines or for information retrieval. 

3. Stop-word removal:

This step essentially removes the stop-words like articles or prepositions, which are commonly used in the text but add no significant semantic value. 

NLP Techniques: 

NLP consists of a wide range of methods and techniques that enable computers to interpret and generate human language.  

The main techniques used for NLP are: 

  • Tokenization: Tokenization is the process of breaking down the main text into smaller units or tokens. This technique lays the foundation for various NLP tasks. The tokens mainly include words or subwords.
  • There are various methods to do Tokenization, which mainly include: 
    • Word Tokenization 
    • Sentence Tokenization 
    • Subword Tokenization 
  • Part-of-Speech Tagging (POS tagging): This process mainly involves assigning grammatical categories i.e. parts of speech, to words in a given sentence. This technique is crucial for understanding the syntactic structure of language. 
  • Some of the methods to POS tagging are: 
    • Rule bases POS tagging 
    • Statistical POS tagging 
    • Machine Learning based POS tagging
  • Named Entity Recognition (NER): This technique mainly involves identifying as well as classifying entities such as names, and locations, contained within text. This is very important for extracting meaningful information from the given unstructured data. It enables the AI tools to understand relationships between entities and the context of the given statements. 
  • Some of the methods to NER are: 
    • Rule bases NER 
    • Statistical NER 
    • Machine Learning-based NER 
    • Sentiment Analysis: sentiment analysis is a technique that involves determining the sentiment in a piece of text. The goal is to identify if the text sentiment is positive, negative, or neutral, which can provide insights into the subjective aspects of the given text. Sentiment analysis can be used, including social media monitoring, customer feedback analysis, and product reviews. 
    • Some of the use cases for Sentiment Analysis are:
      •  Business Insights 
      • Customer Feedback Analysis 
      • Brand Monitoring 
    • Dependency Parsing: This task includes analyzing the grammatical structure of a given text to understand syntactic relationships between words. The output of the dependency parser is a tree structure called a Dependency tree, in which, each word will be a node, and branches represent grammatical relationships between the words. 

    NLP use cases:

    Natural Language Processing can be used in various areas such as: 

    • Chatbots: NLP is the main technology behind chatbots and conversational agents, It enables them to understand user queries and generate appropriate responses. It is mainly used for tech support roles by many companies like customer service chatbots and is also available in smartphones like Siri or Google Assistant.
    • Language Translation: Language translation, is also a subset of NLP. It mainly facilitates the automatic translation of text from one language to another. For example, applications like Google Translate internally use NLP techniques to translate one language to another for web pages. 
    • Question Answering Systems: These systems use NLP to understand user queries and generate relevant responses for the user. IBM Watson is a good example of a Question Answering System. 
    • Autocomplete tools:  These type of tools are trained in order to predict the next words for what has already been typed, mainly in text format. Google is already using this technology by integrating it into Smart Compose, which is already implemented for composing emails in Gmail. 

    Conclusion: 

    Natural Language Processing is a captivating venture where technology combines with linguistics to decode the complexities of human language. From the basics of tokenization to the complexity of sentiment analysis and machine translation, NLP opens doors to a world of applications that enhance our digital interaction. As we are navigating the evolving structure of NLP, the journey has just begun, and the future promises even more exciting possibilities.

    shrinivas-limaye

    Software Engineer