{"id":39,"date":"2020-02-20T16:37:16","date_gmt":"2020-02-20T16:37:16","guid":{"rendered":"https:\/\/jenniferkwentoh.com\/?p=39"},"modified":"2023-04-03T12:01:46","modified_gmt":"2023-04-03T12:01:46","slug":"get-started-with-natural-language-processing-nlp","status":"publish","type":"post","link":"https:\/\/jenniferkwentoh.com\/get-started-with-natural-language-processing-nlp\/","title":{"rendered":"Get started with Natural Language Processing NLP"},"content":{"rendered":"\n

To get started with natural language processing NLP, we will look at some definitions, wins and bottlenecks in NLP. Lastly, we will learn some preprocessing techniques like tokenization, lemmatization and stemming. This article includes code examples in the python programming language.<\/p>\n\n\n\n

I teach the Natural Language Processing track at AI Saturdays in Abuja<\/a>. I hope this blog post and NLP series<\/a> would serve as a refresher for my students and other learners! \ud83d\udc96<\/p>\n\n\n\n

What is Natural language Processing, NLP?<\/h1>\n\n\n\n
 Natural Language Processing, is an interdisciplinary scientific field that deals with the interaction between computers and the human natural language. <\/pre>\n\n\n\n\n\n\n\n
 Some NLP use case scenario includes;<\/strong>
 Speech recognition, Natural language understanding (NLU), Natural language generation, Sentiment analysis, Conversational systems, Machine translation (MT), Information Retrieval systems e.t.c <\/p>\n\n\n\n
NLP Wins and Bottlenecks<\/h2>\n\n\n\n
Let’s start by looking at successes and bottlenecks in NLP. <\/p>\n\n\n\n
Wins<\/h3>\n\n\n\n
Modern computer techniques with natural language are being used today to improve lives and increase efficiency at workplaces. <\/p>\n\n\n\n
Email providers now have NLP powered apps that can scan emails to correct misspellings, extract information and compose emails with better grammar for us. <\/p>\n\n\n\n
A good example is Google smart compose<\/a>, which offers suggestions as you compose an email. <\/p>\n\n\n\n
Another popular example is Grammarly<\/a>, a writing assistant that helps you compose writings with better grammar and checks for spelling errors. <\/p>\n\n\n\n
Social media platforms, with our permission, collect bits of our daily lives and feed us tailored advertisements. <\/p>\n\n\n\n
Speech-powered devices like the Amazon Echo and Google Home can understand and interpret our human natural language commands. <\/p>\n\n\n\n
NLP is super interesting! Isn’t it? \ud83d\ude80 \ud83d\ude0d <\/p>\n\n\n\n
Some of the bottlenecks <\/h3>\n\n\n\n
Computers are built to follow certain rules of logic flow, but humans are not, and so is our communication. <\/p>\n\n\n\n
Some of the tasks that are easy for humans is extremely difficult for the computer.<\/p>\n\n\n\n
How can a computer tell the difference between sarcasm and the original intent of a sentence?<\/p>\n\n\n\n
Human natural language and communication are ambiguous for the computer. <\/p>\n\n\n\n
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Via GIPHY<\/figcaption><\/figure>\n\n\n\n
Our goal is to apply an algorithm to a natural language dataset to train it to understand, interpret, and eventually perform a useful task.<\/p>\n\n\n\n
For us to achieve this, we need to;<\/p>\n\n\n\n
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  1. Assess the grammatical rule of our language (Syntactic)<\/li>\n\n\n\n
  2. Understand and interpret its meanings (Semantics)<\/li>\n<\/ol>\n\n\n\n
    What are the five categories of natural language processing NLP systems?<\/h2>\n\n\n\n
    The five basic natural language processing categories include lexical analysis, syntactic analysis, semantic analysis, discourse integration, parsing, and pragmatic analysis.<\/strong><\/p>\n\n\n\n
    Lexical Analysis<\/h2>\n\n\n\n
    Lexical analysis entails trying to understand the meaning of words while also considering their usage context and how they relate to other words.<\/p>\n\n\n\n
    Lexical analysis is often the first step when dealing with NLP data pipelines.<\/p>\n\n\n\n
    Syntactic Analysis<\/h2>\n\n\n\n
    Words are arranged in a way that grammatically makes sense.<\/p>\n\n\n\n
    Semantic Analysis<\/h2>\n\n\n\n
    Semantics is the meaning expressed by a given input text.<\/p>\n\n\n\n
    Semantic analysis is one of the difficult tasks in natural language processing because it requires complex algorithms to understand and interpret the meaning of words and the overall structure of the given sentence.<\/p>\n\n\n\n
    Semantic analysis may include techniques such as:<\/p>\n\n\n\n