This blog talks about the top 14 NLP project ideas for beginners in 2022, where you will go through a brief introduction to natural language processing and its phases, along with the skills required to become a successful NLP engineer.
Natural Language Processing, or NLP, is an essential tool of artificial intelligence. It is like a connecting neuron between humans and machines.
NLP, AI and machine learning go hand in hand when it comes to building the future of technology.
Now let’s take a look at how NLP plays a vital role in the global tech industry and how it has shaped the views of people so far.
Key Takeaways
- Learn about Natural Language Processing and the five phases involved in how a computer understands human language and behaviour.
- Educate yourself on the prerequisite skills to becoming an NLP engineer.
- Dive into the various project ideas using Natural Language Processing.
What is Natural Language Processing?
Natural language processing, or NLP, is the ability of a computer to understand and interpret human language.
A sentence in human language is a group of words. In NLP, the phrase is broken into fragments so that the computer can analyze each part with context.
The machine learning algorithm learns to study millions of texts in natural language processing to understand the context.
In NLP, there are five steps involved in the process of how human language is broken down and understood by a computer:
- Lexical Analysis
- Syntactic Analysis
- Semantic Analysis
- Discourse Integration
- Pragmatic Analysis
Lexical Analysis
In the initial phase, breaking a paragraph into lexemes is done. A lexeme is a unit of several words.
Syntactic Analysis (Parsing)
This phase is like Grammarly in NLP. It checks for grammatical errors and rejects them. For example, a sentence like ‘Pooja is go Goa’ will be left because it makes no sense.
Semantic Analysis
Semantic analysis has to do with finding out the meaning of every word in the sentence.
Discourse Integration
Discourse integration identifies the relationship and meaning between the sentences.
Pragmatic Analysis
The final phase helps identify the sentence’s intent and effect. For example, the sentence ‘switch on the lights’ is an order.
Skills Required to Become an NLP Engineer
An NLP engineer combines technical skills and linguistics to make devices more interactive. The essential requirements for becoming an NLP engineer/ architect are below.
1. Concepts and Methods in Machine Learning
Understanding neural networks, Bayesian networks, and maximum entropy is an essential skill to master as an NLP engineer.
2. Proficiency in an Object-Oriented Programming Language
Experience in Python or R coding is essential as it is a vital criterion on this list.
3. Text Representation Techniques
An NLP engineer should know how to convert words into numbers for machines to understand. Apart from this, learning language models like the N-grams and Bag-of-words models is a bonus.
4. Familiarity with Big Data Frameworks
Big data has become a necessity rather than a choice for most companies. With NLP, big data can create powerful machine learning training models and better understand consumer behavior. Hence an NLP engineer must know how to work with big data frameworks like Hadoop and Spark.
Take a Glimpse: 10 Best Computer Science Capstone Project Ideas
Top 14 NLP Project Ideas for Beginners
1. Comments Classification
Large organizations often have to deal with negative feedback on social media and Google comments. You can write an NLP program to classify positive, negative, and neutral words.
2. Predictive Text Generator
A predictive text generator like Smart Compose for Google Docs takes cues from two previous words to generate the next word or sentence.
3. Personal Voice Assistant
Another exciting project to work on could be your version of Siri or Alexa. A virtual voice assistant is created using artificial intelligence and natural language processing. NLP is used to understand and process human speech.
Here is a Github link to get you started.
4. Virtual Chatbot
A regular chatbot has a fixed number of replies or answers to specific questions. A customer support chatbot functions using artificial intelligence and natural language processing to understand the customer’s needs and provide proper technical support to their queries.
A virtual chatbot can be coded using the ChatterBot python library.
Click here to learn more about how to create your chatbot.
5. Identifying Spam Messages and Emails Using NLP
Have you ever wondered how Gmail marks the important mails according to your preferences and sends unnecessary emails to the spam folder?
NLP methods that identify and sort the emails according to the sender and based on how the user treats each email and how it is being addressed are used. Spam identification is executed by collecting spam emails and training the algorithms to identify and sort accordingly.
6. Targeted Advertising
NLP is widely used for targeted advertising on numerous social media platforms to provide a better user experience. It is feeding the users with relevant information based on their interests.
Natural language processing is used to gain valuable data from social media posts and audio and video files to create complex algorithms according to user behavior.
7. Urgency Crisis
An indispensable project idea to work on would be to train an NLP model to detect users’ emergency texts and voice notes and notify the relevant authorities. It would require the model to go through the previous texts to understand the type of emergency.
8. Language Translation
A simple segway into the world of NLP to learn and understand the subject would be to create a language translator.
You can use Python libraries or a Recurrent Neural Network (RNN) to kick start this project.
9. Intent Classifier
One of the most exciting things about NLP is the ability of the computer to understand what you are asking and give you an accurate answer.
Intent classification is a branch of natural language understanding that deals with understanding the user’s request and identifying its intent.
Voice assistants like Siri and Alexa use deep learning algorithms to learn phrases and perform the action it is trained to perform.
Learn more about this project here.
10. Image Description Generator
Image Description Generator is a simple project for beginners which involves deep learning algorithms and image processing methods to identify the components and objects in the image converted to sentences using NLP techniques.
This project could come in handy for blind people who can have their books or road signs read out to them through smart glasses.
11. Topic Identification
Topic Identification is an NLP project for people starting in the field where you can learn about NLP algorithms in detail. You will need a document and relevant NLP algorithms to name the document based on its contents to carry out this project.
You will also learn to use unsupervised machine learning algorithms as well.
12. Blog Summarizer
In today’s fast-paced world, people don’t spend much time reading blogs (please read this one, though). Instead, they only need the crux or summary to get the information.
There are two types of approaches that you can take to this project. The first approach is an extractive method which is simple and involves storing all the essential words and the frequency of those words in the document to create the summary.
The second method is abstractive, complex, but compelling. It involves using deep learning algorithms to exchange the longer sentences with the shorter ones with the same text semantics to provide more information.
Read more about the Abstractive and Extractive methods.
13. Resume Examining System
A resume examining system should be able to categorize the resumes accordingly after reading the data of each data. It will save time for the human resources department and make hiring more efficient.
This project can be done using NLP methods and machine learning algorithms. You will learn how to use Optical Character Recognition. Since all the resumes must be in pdf format, you will also learn to extract the text.
14. Questions Tagger
Question and answer websites like Quora and Github have faced issues of people using the wrong tags in their questions. This project aims to create an automatic question tagger that reads the question and uses the proper tags according to the keywords.
Beginners can use Stacksample to implement this project. It is a large dataset that has questions, answers and tags. You can also use the Python Pandas library to analyze the tags.
Conclusion
Now that we’ve reached the last part of this blog, I hope you got what you were looking for regarding the best NLP project ideas.
Natural Processing Language is a field that is actively looking to hire engineers to build more models to solve various problems.
The best way to test your skills is by taking up a project and completing it. You will realize that there will always be something new to learn and implement.
All the best, and let the creative juices flow!!
Frequently Asked Questions
1. What is parsing in Natural Language Processing?
Parsing is checking if the text makes sense grammatically.
2. What are some popular models besides Bag of Words?
There are other models like Latent semantic indexing and word2vec.
3. How can machines make meaning out of language?
Stemming is used, where the word is approximated to its root by removing the verb and plural forms.
