If you are planning to do an MS or PhD and your domain is Natural Language Processing then there are many ideas in this domain. After my research, these are some of the top research ideas to use in the NLP domain in 2024.
- Contextual Understanding
One of the primary challenges in NLP is developing models that can get context and generate responses that are not just grammatically correct but also contextually relevant. Current efforts are focused on enhancing language models with better memory and reasoning capabilities to achieve this goal.
Project Idea
Contextual Chatbot for Customer Service“: Develop a chatbot that can understand and respond to customer queries in the context of previous interactions, providing more personalized and helpful responses over time.
- Multimodal Understanding
If you work on deep learning or have an idea for deep learning, then you know very well that the more data you have for deep learning, the better model will be trained. The amount of data that is required is very large, and the model does not train as well on less data, so the concept of a multi-model comes into consideration for this problem. NLP is spreading with other modalities such as images, videos, and audio. Researchers are working on integrating these modalities into NLP models to enable tasks like image captioning, video summarization, and more.
Project Idea
“Multimodal News Summarization”: Create a system that can summarize news articles using both text and images, providing users with a comprehensive overview of the news story.
- Explainable NLP Models
Machine learning is interconnected with statistics and data mining, where we have data in numerical form. Actually, there are statistical models behind machine learning. On the other side, deep learning’s actually derived from neural networks, so neural networks are derived from neurons in the brain. Deep learning is like a black box—a black box inside which we don’t see much. DL has not been explained as well as machine learning has because de-learning is quite a latecomer to machine learning.
As NLP models become more sophisticated, there is a growing need for them to provide explanations for their decisions and outputs. Efforts are underway to design models that can offer transparent and interpretable explanations.
Project Idea
“Explainable Sentiment Analysis: Create a sentiment analysis model that can explain its predictions, highlighting the key features in the text that influence its sentiment classification.
- Domain-specific NLP
Whenever you propose an idea within your master’s or PhD thesis, the focus is more on the problem to be solved and not on creating a model from scratch. Creating data and gathering it is also a very difficult task, and the data is relevant to any one category or domain. To build our model on this data, we need to build our model according to that domain.
Tailoring NLP models and techniques for specific domains such as healthcare, finance, or legal can significantly improve performance and accuracy in these specialized areas. Researchers are developing domain-specific models to address the unique challenges posed by these domains.
Project Idea
“Legal Document Summarization”: Build a system that can summarize legal documents, extract key information, and reduce the complexity of legal texts for easier understanding.
If you need any help in NLP or related domain you can contact me