NLP, or Natural Language Processing, is a branch of artificial intelligence that studies how computers interact with humans. Beginning software development, students may find it challenging to locate NLP projects that meet their educational goals. We’ve compiled a list of starter samples. The easiest way to get started is to work on NLP projects.
What is Natural Language Processing?
In computer science, natural language processing (NLP) refers to the discipline of artificial intelligence (AI) that allows computers to interpret text and spoken words, similar to human beings.
We think theoretical knowledge will not be enough in the actual world of business. Thus, we take a hands-on approach to natural language processing tutorial. We will discuss some intriguing NLP projects in this post.
Why construct NLP projects?
Before considering applying for software development jobs, aspiring software engineers must have experience working on projects. Doing real-world projects is the most excellent approach to refining your talents and turning theoretical information into real-world practice.
NLP is a computer science branch that analyzes and represents human language. It allows computers to use context cues to react in the same way humans do. Spell check, autocomplete, spam filters, voice text messaging, and virtual assistants like Alexa, Siri, and so on are all examples of NLP in our daily lives. With NLP projects, you’ll not only be able to test your abilities, but you’ll also acquire valuable experience that may help you advance your profession.
As a result, the following NLP projects are suitable for beginners:
Ideas for an NLP project
Beginners, intermediates, and specialists will all find something useful in this collection of NLP student assignments. Using these NLP projects, you’ll learn all the practical skills you need to thrive in the workplace.
In addition, if you’re seeking final-year natural language processing projects, this list might help. Without further ado, here are several NLP projects to help you build a solid foundation and climb the career ladder.
NLP project suggestions are included in this list to assist you in going ahead.
1. A chatbot for customer service
A customer service bot is one of the most exemplary NLP projects for students to work on. A chatbot responds to customers’ common questions with pre-programmed replies. These bots cannot comprehend complex queries. Artificial intelligence and machine learning technologies have overcome these constraints. They can also produce questions without pre-written solutions.
Reply.ai, for example, has developed a bespoke ML-powered chatbot for customer service. They claim that an average firm can handle around 40% of its incoming support queries using its software. ‘ Let’s look at the model needed to create a project based on this item.
Microsoft’s DialoGPT, a pre-trained conversation answer generation model, may be used for this purpose. It uses Hugging Face’s PyTorch Transformers and OpenAI’s GPT-2 (both from OpenAI) to answer text inquiries. Cortex is capable of running a full DialoGPT deployment. To clone an existing repository, you may do so from various sources accessible online. You may improve customer support by connecting your back-end API to your front-end UI after it has been implemented.
Our learners also read- Which are applications of Natural Language Processing in AI?
2. A linguistic identification.
You may not have noticed, but Google Chrome can identify the language of a web page. It can accomplish so by using a neural network model for language identification.
Those who are new to NLP will find this project quite beneficial. Textual analysis entails searching for standard terms across languages and dialects and employing numerous languages in the same piece of writing. However, with the help of machine learning, this work is much easier.
The fastText concept from Facebook lets you create your unique language identification. An expansion of the word2vec tool, this model employs word embeddings to grasp a language. You may use word vectors to map a word depending on its semantics, for as, by removing the “male” word vector from the word vector for “king” and then adding the word vector for “female.”
FastText’s unique feature is that it can decompose complex words into n-grams and comprehend them. Whenever it is presented with an unknown word, it examines the smaller n-grams, or familiar roots, existing within it to determine its meaning. Deploying fastTExt as an API is a snap when you use online repositories for guidance.
3. Autocomplete enabled by Machine Learning
Key value lookup is the most common method for autocomplete, in which the user’s incomplete keywords are matched to a dictionary to provide a list of potential words. Using machine learning, you may enhance this function further by anticipating the following few words or phrases in your message.
Instead of accessing a static dictionary, the model will be trained using user inputs. For example, Gmail has a “Smart Reply” feature that produces suitable responses to your emails depending on your information. Let’s have a look at how you can implement this functionality.
The Roberta language model is appropriate for this undertaking. When Facebook implemented it, it improved Google’s BERT method. In many NLP measures, it outperforms competing models because of its training approach and computer capacity.
4. Predictive text generators
NLP projects like this one are always intriguing. Has AI Dungeon 2 ever caught your attention? The GPT-2 prediction model was used to build this classic text adventure game. Using a library of interactive fiction, the game learns how to develop tales and then uses that knowledge to create its own. It’s early days, but machine learning will profoundly impact how people interact with games within a few years. Learn how you may use natural language processing with python in the creation of video games.
For this project, you should use Open AI’s GPT-2 model. A pre-trained model may be easily implemented and interacted with afterward. For your next NLP project, this is perfect!
5. A media player
Using a media monitor is an excellent way for students to start hands-on NLP projects. In today’s business world, customer satisfaction is a crucial determinant of the success of your company’s brand. Customers may publicly express their thoughts and opinions about your items using social media and other digital channels. As a result, companies now seek to monitor internet mentions of their brands. Machine learning has provided the most significant boost to these monitoring efforts.
You may get a sentiment timeline from the analytics company Keyhole, which filters all your social media postings and shows you if they are positive, neutral, or negative. An ML-powered search of news websites is also possible. In the financial industry, for example, firms may use NLP to measure the attitude of digital news sources regarding their company.
Customer service may also benefit from media analytics of this kind. By monitoring and gaining insights from actual news events (like oil spills), financial service providers may help customers who own assets within the oil and gas sector.
Conclusion
In this post, we’ve discussed various NLP projects that you may use to create ML models, even if you have a basic understanding of programming. We also spoke about the goods’ usability in the actual world. Using these themes as a springboard can help you advance your professional and entrepreneurial endeavors.
You can only learn how infrastructures operate in the real world if you use the right tools and put them to use. After reading our NLP projects guide, put what you’ve learned to the test by creating your natural language processing projects.
