10 best NLP projects to boost your portfolio
In this blog, we are going to discuss the 10 best NLP Projects that you can create and make your portfolio attractive in the eyes of the interviewers.
Ever thought about how Alexa understands what you are saying or how can it be intelligent to understand your words? Natural Language Processing is the answer!!
Natural language processing (NLP) refers to the branch of computer science — and more specifically, the branch of artificial intelligence or AI — concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP is really an exciting field due to its wide range of applications that make our life easier. Knowingly or unknowingly, you are interacting with NLP applications on daily basis.
You must have used Google Assistant or Amazon Alexa. Did you?
Yeah, both are NLP applications. You might have encountered chatbots on many websites which automatically respond to you according to your question. Also, when you write an email, automatically your grammatical mistakes are corrected if any.
Seems interesting, right? This is the impact that Natural Language Processing is making on our lives to make it easier.
Kindly share this blog with anyone even if he/she doesn’t understand English!! Yes, you read it right. A person who does not know English will be able to read this blog. How? NLP again comes to the rescue. Language Translation is also an amazing application of NLP. You too can create these amazing applications using NLP. Developing real-world projects is the best way to hone your skills and get practical experience. I bet you will really enjoy working on these projects.
What are you waiting for? Let us start discussing the best NLP Projects you can also build!!
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1. Text Summarization
When you open the newspaper, do you start reading every new article? I guess not. You first read the summary of the news article to get an overview. But most of the summaries written in present times are written manually.
What if I tell you that AI can write the summary for you if you just provide the news article? Doesn’t it sound fascinating? Yes, Text Summarization is one of the most interesting problems you can solve in NLP. It is especially useful when you have a huge amount of text. It’s so time-consuming to write the summary in that case in an effective manner.
It has applications in various fields like media, entertainment industry, academics, etc. Have you heard of the Inshorts app? The application uses NLP to get the summary of the news articles saving money and cost. So, you can see that NLP is not just saving time, but also cutting costs to increase the profits of the company.
There are various libraries and algorithms to create a text summarizer. One of them is Gensim Library which uses the Textrank algorithm to create a summary. Advanced techniques like GPT and Transformers are also really good at text-summarization tasks.
You can go through the following research paper for a better understanding of Text Summarization.
2. Plagiarism Checker
With the advent of the Internet, a huge amount of information is shared daily. Since all the information and content are easily accessible by everyone, many people take advantage of this and simply copy other people’s content leading to an increase in plagiarism. This is a critical issue especially in the domain of academic research. Many people steal the ideas and work of others without giving a deserving acknowledgment.
Due to the huge amount of content, it is not practically possible to manually check for plagiarism. That is why we need some method or technology that can check whether the content is plagiarized or not. NLP is exceptionally brilliant at this task.
Latent Semantic Analysis (LSA) is an NLP technique that can effectively check for plagiarism.
3. Conversational Bot
This is probably the most applied NLP application in the real world. You will find chatbots interacting with humans now in every field. You might have sometimes asked Google Assistant about the weather. Or have enquired about any course on an ed-tech website. These are nothing but amazing applications of NLP only. The chatbots can respond to the user according to the question asked by the user.
The best part is they are not that difficult to create. There are various frameworks available that lets you build intelligent chatbots easily.
Some other frameworks available are:
- Google Dialogflow
- Amazon Lex
- Microsoft Luis
- IBM Watson
- RASA
4. MCQ Generation
Provide a text and get a set of MCQs. Wait, what! Is it possible? Yes, Natural Language Processing is capable of doing that. Such an alluring application!
Just imagine a teacher who wants to take an MCQ test. How easy it would be for the teacher to get a set of MCQs by just providing the text. This saves a lot of time and manual hard work.
For achieving this task, we can use a BERT extractive summarizer or Transformer model. Keywords can be extracted using the Python Keyword Extractor library.
This is a good read that will enhance your knowledge regarding this project.
5. Grammar Correction
Wish to write an article or a letter or any other document but are unsure about your grammatical skills? Having too many grammatical errors creates a bad impression of yours in front of the reader. Well, do not worry. I bet you will be able to write the document grammatically error-free. Thanks to the advancing field of NLP.
You would have noticed while writing emails that it suggests the correct grammar automatically and also corrects the spelling. This has made our work so convenient that we no longer need to worry about grammatical mistakes.
Grammarly is one such application that autocorrects spellings and also suggests us the appropriate grammar. It can even be downloaded as a chrome extension.
You should train your algorithms with a large dataset of texts that are widely appreciated for the use of correct grammar. For training, it’s a must that you perform necessary NLP techniques like Lemmatization, Removal of stop words/ irrelevant words, Removal of punctuations, etc.
6. Language Translation
Language Translation is an astonishing application of NLP. You can translate any text in any language to the language of your choice, say French to English. We don’t necessarily need to take help from a person having mastery in the language you don’t understand. You can simply feed the text and the NLP model will translate it for you.
It is used in so many applications like Facebook and Instagram. Facebook and Instagram show the text after language translation also. It is so convenient to interpret the text written in other languages. The best part is that the error rate of these models is very low.
The NLP Model first identifies the language to convert. You just need to train the model with a huge dataset having text along with its language. You can get a readily available dataset also for this project.
7. Resume Parsing Application
There is hardly any domain left untouched by NLP. It has extended its presence in the HR Domain as well. Companies receive too many resumes for a job profile. It is manually very time-consuming and sometimes inefficient too to extract the required information from each and every resume.
A resume parser is an NLP model that can extract information like Skill, University, Degree, Name, Phone, Designation, Email, other Social media links, Nationality, etc. irrespective of their structure in no time. There are many resume parsers available these days online. Well, you can create your own too.
Regular Expressions (or Regex) can help extract the required section from the resume. BERT Named Entity Recognition (BERT NER) can then be used to create the model.
8. Image Caption Generator
It is a hot research area in Artificial Intelligence. It generates a textual description for an image. This requires assistance from Computer Vision techniques also to understand the image.
You can see the above image to see how accurately the model is generating the captions. This saves a lot of time otherwise doing it manually will incur the cost to the company.
CNN or transfer learning models like Inception, VGG16, ResNet50, GoogleNet can be used to classify the image. And RNN/LSTM as the language model to encode the text sequences. The encoder can combine both the encoded form of the image and the encoded form of the text caption and feed it to the decoder.
9. Toxic Comment Classification
A must application in the era of social media!
Many times, you might have seen celebrities and cricketers encounter lots of criticism on social media. While constructive criticism is good but posting abusive comments lowers the morale of a person and also puts a negative impact on young children especially using social media. That is why it is crucial to tackle this issue. NLP models can detect toxic comments. These comments can then be automatically deleted so that it doesn’t get seen by many people.
The data for this project can be found here.
10. Text Generation
Do you want to produce a text automatically? Well, you can create your own text generation tool.
It can automatically generate a text and also complete a sentence using RNN/LSTM techniques.
An amazing application of it is GitHub Copilot co-developed by OpenAI and GitHub. It was trained on codes on millions of public repositories on GitHub. It is so powerful that it can auto-generate the code you want. NLP is able to understand what the programmer wants and it will automatically complete the code. For example, if you want the code to generate the code for checking if a number is prime or not, simply write it in the comment, and GitHub Copilot will give you the code. Isn’t that powerful?
Conclusion
So, we discussed the top 10 NLP Projects that you can create to stand out from the crowd. You will get a lot of hands-on experience working on these projects. You will feel confident about your NLP skills. These projects can be used in real life too.
NLP has widespread importance in probably every industry now. This technology is saving a lot of time along with money as well. What better can you have than to save the costs of the company and increase your profits using technology?
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