Step By Step Explanation of NLP Data Pipeline

Natural Language Processing is Hot and most research topic of today's technical revolutionizing world. Every computer science and artificial intelligence solution enables machines to make human tasks easy and directly or indirectly machines have to interact with humans in any form may be written, voice, or visual form. So NLP is a technology that enables communication between humans and machines successfully leveraging many real-world solutions to create a great impact on society. If you are a complete beginner then I would like to request you to please read my previous article that covers the complete landscape of NLP. You can find it on my profile and it will provide you a complete understanding of NLP and help you think more powerfully in NLP progressing your journey.

Step By step NLP data Pipeline

In this article, we will cover what is a basic pipeline implementing any NLP use case where you will learn step-by-step procedures along with different methods used at each stage. The article mainly covers the theoretical concepts of each procedure which is very important to understand first before implementing them so the practical implementation is showcased in the NLP project where you will gain hands-on experience practicing each step with python.

What is NLP Pipeline

NLP Pipeline is a set of steps followed to build an end-to-end NLP software. NLP Pipeline is basically a step-by-step procedure that helps us to understand the NLP project lifecycle and as a beginner, it is very important to get how real-world projects are initiated and progressed till the final stage. The machine learning pipeline we built consists of steps like cleaning, and processing data. The same pipeline is built for NLP but the steps we used to deal with text data are changed and that's what we are here to study in the further heading of this article.

Points to Remember

  • It's not universal - The pipeline we are going to study in this article is not a universal pipeline for every NLP project. The pipeline will be exactly the same as small-scale NLP projects like sentiment analysis, text classification. But not the same in large-scale and some production-based projects. The steps we include in the pipeline will be there in every project and it may be in any order.
  • Deep Learning Pipelines are slightly different - NLP models work closely with Machine learning as well as with deep learning. The deep learning models for NLP have some different methods. here we are focusing on beginners so our focus is on the machine learning-based approach and its pipeline.
  • A pipeline is non-linear - The steps in the pipeline are in a non-linear order. sometimes the performance is not good after modeling so we again bounce back to the feature engineering step.

so now let's start studying each step in the pipeline.

Step by Step Explanation of NLP Pipeline

1) Data Acquisition

The foremost and first requirement of any data science project is data. let's take an example to understand this in deep. suppose we are working in an organization and the aim is to implement an NLP model for sentiment analysis for some organization customers. so the first thing we require is data, now data can be present in different ways that you want to access first. so let us discuss each case under data Acquisition.

A) When data is available in an organization

Most of the time organization provides the data but can be in different forms and you need to access it.

  • The data is directly available in your system in any file format like CSV, txt, excel.
  • The data is available in the organization database and you have to use SQL queries to access it.
  • One is data is not available in sufficient amount then you use techniques like data augmentation like if it is image data then you increase data by rotating, flipping image. If it is text or tabular data then you add some noise.

B) Data available from External resources

Sometimes you are supposed to import the data from external sources and it can be anything.

  • A dataset is available through some public resources or online data repositories.
  • Data is available online on the website and if it provides through API then you need to request at the API to get the data.
  • If API is not available then you are sometimes required to perform web scraping to scrape and store data in a database or local storage.
  • Sometimes data is also ingested in audio forms because most of the users give feedback on customer care numbers so data is also taken from audio using speech to text technology.

C) No Data is available

The problem arises when no data is available then you have to move forward very intelligently. To get the data you have to talk with the product team and conduct surveys between loyal customers to get the right feedback. After that, you have to label those data manually or use some heuristic approaches like regular expression then move to a machine learning-based approach.

2) Text Preprocessing Or Data Preparation

Now we have the data and move to the second stage of preprocessing. after having data our aim is to prepare the data that can be fed to the machine learning model so we perform different text cleaning methods and preprocessing steps to bring the text in the correct order.

A) Text Cleaning

Text cleaning basically means removing unwanted text from the data and it can be anything.

  • When data obtained from a website using API or web scrapping HTML tags found its existence inside textual data.
  • social media data contains emojis so we need to remove them or encode them in Unicode.
  • It is a human tendency to make mistakes and sometimes while typing most of the time we type wrong spelling, some characters are missed which is also known as fact fingering typing.

For text cleaning mainly use a regular expression(python re module) and basic user define functions to deal with specific tasks. below is a practical example of removing HTML tags.

text = "<h2>HTML Element</h2><p>The HTML Element defines superscript text.</p>"
import re
def striphtml(data):
    p = re.compile(r'<.*?>')
    return p.sub('', data)

same we also use for encoding emojis.

emoji_text = "I was really amazed by watching itπŸ™‚. All the best to you πŸ‘."

B) Basic Text Preprocessing methods

There are different methods in which some methods are compulsory to use in each use case and some methods are optional to use.

  • Tokenization is a step in which a paragraph is broken down into sentences or words and this method is performed in each NLP use case.
  • Converting text to a particular case(Lowercase or uppercase) to make text consistent and avoid common mistakes.
  • Removing punctuation, digits, extra spaces are some methods that are optional to perform in NLP use cases.

C) Advance Preprocessing methods

These methods are used in some specific NLP use cases because different methods give excellent results.

POS tagging is a wonderful method when we work with text generation, summarization, grammar check kinds of things when you want machines to make understand the word as a noun, adjective, adverb, or any other part of speech.

Parsing is a method used for question answering systems like chatbots where sentence generation is to understand.

3) Feature Engineering

Now we have prepared our text data and are ready to convert it into a form that is ready to fed model. In simple words process of converting text to numbers is feature engineering. There are different approaches to do so and it is a very interesting method.

In a machine learning-based approach after preprocessing, you have to apply your intelligence in generating features from the text. Indeed in the deep learning approach after preprocessing, you only have to feed text data to the model and it will generate features according to it which is also known as text embedding. The advantage of using a machine learning system is you know everything and the disadvantage is it is a little bit complex to increase performance. On the other hand advantage of deep learning is performance is very good but The disadvantage of the deep learning approach is we did not know how and what features it has generated. In short, interpretability is loosed in the deep learning approach.

4) Modelling

At this stage we prepare the machine learning model with default or user-defined parameters as per requirements and the performance of the model is evaluated on unseen data. So this stage can be understood in further 2 sub-stages as Model building and model evaluation.

  • In the model building, we try different Machine learning models like Naive Bayes, SVM, etc which proves to be efficient on textual data.
  • After that, we evaluate the model in 2 processes known as intrinsic evaluation and extrinsic evaluation. Intrinsic evaluation basically means evaluating the model on a training dataset and sometimes validation dataset evaluation is included in it. And extrinsic evaluation means evaluating an unseen dataset(Test dataset) or taking data from an external source like from some user.

5) Deployment

Model is prepared evaluated and ready to serve our audience. deployment has three main stages is deploy, monitor, and update the changes.

  • In the deployment stage, you simply put your model on any public cloud-like Heroku, AWS, Azure, etc, and get a user access locator(URL) for your model.
  • Monitoring simply means keeping a regular eye on your model and saving the results in any form of how users are using your model. In this stage, you analyze the model performance and how it serves your audience. After this, you find some changes to make it more effective.
  • After monitoring in the update phase you make data changes and again train and deploy your model by changing the dataset.


This was the complete NLP pipeline we have covered in this article. NLP today is the most research and Hot topic in many use cases. After reading this article I hope that your mind is now open to thinking openly and deeply in NLP if you have a little bit of practical knowledge and if not then as a beginner I hope you have a wonderful experience reading it and it has created an excitement in further data science journey. If you have any doubts or feedback, feel free to share them in the comments section below.

Thanks for your time 😊

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