Natural Language Processing (NLP) | Getting Started with NLP

Hello, Have you ever wonder about a question in a mind that how, machines speak, understand, and respond to us in a different sort of human language. what technique is working behind it which makes it enable to perform all these tasks? Maybe you are using Siri or Goggle Assistant which helps to perform most of your tasks within a minute.

The technology known as Natural Language Processing(NLP) is used to solve all the tasks related to human language. NLP is not a science, rather it is an applied science. It is an engineering discipline that empowers the use of Artificial Intelligence, Computer Science, and Computer Linguistics to understand Human Language.

In this tutorial, we are going to understand Natural Language processing and its benefits with real-world Applications where it is currently used and where Research and Advancements are going on with respect to particular domains, and how it is helping to leverage society growth to the next level.

What is Natural Language Processing

Natural Language Processing is a technology used by machines to understand, analyze, process, and respond in a human language. Now you know there are thousands of languages with lots of noise(uncertainty) spoken by humans and how fast our machine is that it responds us to fast.
The biggest use case you can see is in Chatbots which are almost used by each organization to maintain their customer base and solve basic customer queries.

The second Myth is that it is not Deep Learning or Machine Learning. They can be used to solve a large number of problems related to NLP.

Advantage of NLP

1) It helps users to develop Question Answering system
2) NLP helps computers to communicate with humans in their language
3) It is very time efficient
4) The information which NLP provides is relevant to data, it does not provide unwanted or unnecessary documents.

Applications of NLP

  1. Chatbots: The working concept behind it is NLP only, which we talked about
  2. Recommendation System: There is various recommendation system which understands the user's behaviors based on different methods that use the NLP. we can see recommendations on e-commerce sites, youtube, Netflix, etc.
  3. Machine Translation: Google translator used to translate from one language to many multiple languages
  4. Summarization: Automatic Summarization of multiple documents and retrieve useful information comes under the application of NLP. 
  5. Spam Detection: NLP is mostly used in cybersecurity to catch fraud. spam detection is very good to use the case of NLP used by GMAIL and SMS services.
  6. Spelling Correction: today we all use Grammarly for checking and correcting grammatical errors in our articles, essay, blogs, or while writing any message on social media sites.
  7. Speech Recognition: We all love to search anything using voice search instead of typing. so how does this happens? NLP is standing behind it to support the system to recognize your voice and process it to extract meaningful information for you within a second.

Now let's understand the phases of NLP. The complete process on which NLP particular task runs in step-by-step phases.

Phases of NLP

There are following five phases of NLP

1) Lexical Analysis

It is also known as Morphological analysis. This is the first phase of processing natural language in which it takes the complete text and divides it into small lexemes. Lexemes mean to divide the text into paragraphs, sentences, or words.

2) Syntactic Analyzer

It is the second phase of NLP. The main goal of this phase is to check the grammar, word arrangements mean to check that sentence is well-formed or not and break it up that represents the syntax relationship between the words.
For example, Football played the boy. 
The particular sentence will be rejected by the syntax analyzer or parser.

3) Semantic Analysis

It is the third phase of NLP. It aims to draw the exact meaning of the words or we can say as to check the dictionary meaning of the word from the text and then check its relationship with the sentence.
For example, the semantic analyzer will reject the words as happy lemon.

4) Discourse Integration

Discourse Integration depends upon the sentences that proceed it and also invoke the meaning of the sentences that follow it.

5) Pragmatic Analysis

It is the fifth and last phase of NLP. it simply fits the words that exist in an actual context.
For example, pick the odd one out is interpreted as a request instead of an order.

How to Build an NLP pipeline

There are the following steps used to build an NLP pipeline and solve a particular NLP use case. It is not necessary to use all these steps in all the problem, there are some of the steps which are not required in a particular use case.

Step-1) Text Preprocessing

This is the very first step which includes removing all the unwanted text from your data by cleaning it up and prepare for further analysis.

Text preprocessing deals with the following steps.

1) Noise Removal: Noise removal further includes many methods to remove all the unnecessary details from data.
  • Sentence Segmentation(Tokenization): The first step is to tokenize the data into words or sentences.
  • Removing Stopwords: removing common words from the text which does not put any importance is necessary which helps to reduce the size of the corpus and train the model fast. stopwords include words like a the, their, was, were, etc.
  • Remove Whitespaces: Remove the extra spaces from the data.
2) Lexicon Normalization: Another type of noise in textual data is multiple representations of a particular word. for example like plays, playing, played are all exhibit a common meaning as to play. so to make this understand by the model we have to extract root words from all the words in textual data.
There are 2 methods use to achieve this.
  • Stemming: Stemming is a rule-based process of striping suffixes from a word. (es, ed, s, ing, etc)
  • Lemmatization: It is an organized and step-by-step procedure of obtaining a root word from a word.

 Step-2) Dependency Parsing

Dependency parsing is a process to find the relation of all the words from each other.

Step-3) POS tagging

POS stands for part of speech which includes all the 8 parts of speech as noun, verb, adjective, adverb, pronoun. It indicates that how a word functions with its meaning as well as grammatically within the sentences. A word has one or more parts of speech based on the context in which it is used.

Step-4) Named Entity Recognition(NER)

It is a process of detecting that a particular word is a name of a person, the name of an organization, money-related information, etc.

Step-5) Chunking

Chunking is used to collect an individual piece of information and group them into bigger pieces of sentences.

NLP Libraries

Python is known for having a huge collection of many python libraries and for performing NLP tasks there are also many prebuilt libraries that include many functions for performing all these tasks that we talked about.
the list of NLP libraries include:
  • Natural Language Toolkit(NLTK): It is a complete toolkit for all NLP techniques. It is developed for practicing and studying NLTK purposes and very little used in production. we use Spacy.
  • Spacy: SpaCy is an open-source NLP library that is used for Data Extraction, Data Analysis, Sentiment Analysis, and Text Summarization.
  • TextBlob: It includes many functionalities which are easy to use like sentiment analysis, Named entity recognization, pos tagging.
  • Gensim: Gensim works with large datasets and processes data streams.


Natural Language Processing is a field of Artificial Intelligence that deals with the processing of Natural Language and helps in building more robust models to make the human task at ease. I hope you enjoyed reading this article and understand the power of NLP. There are many types of research going on in this field because understanding human language is so complex task and at current we do not have a system or a chatbot that can understand sarcasm so coming years in the field of NLP going to be bright and enjoyable.
Keep Learning, Happy Learning
Thank You!

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