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HomeBlogData ScienceTop 20 NLP Project Ideas in 2024 [with Source Code]
Natural Language Processing or NLP is a part of Machine Learning focusing on spoken language with loads of data. Interactions between people and machines using natural language are the subject of the interdisciplinary field of natural language processing (NLP). NLP has a wide use in industry like conversational voice assistants (Amazon Alexa, Apple Siri) use NLP, as do sentiment analysis tools (Amazon recommendations), language translation tools (Google Translate, IBM Watson) etc, and so on. Building a career in this field might require you to have a certification in data science. If you are wondering about the Data Science course fees in India, we got you covered!
This blog tackles a wide range of intriguing NLP project ideas, from easy NLP projects for newcomers to challenging NLP projects for experts that will aid in the development of NLP abilities.
In addition to introducing the attention mechanism, the ingeniously titled "attention is all you need" study made it possible to develop potent language models, such as the Universal Language Model (ULM-Fit), Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer (GPT) which are in a rapid pace of development. NLP Project Ideas are essential for understanding these models further. Natural Language Processing projects are industry-ready and real-life situation-based projects using NLP tools and technologies to drive business outcomes.
Working on real-world NLP projects is the best way to develop NLP skills and turn user data into practical experiences. While looking for employment in the NLP field, you'll be at a significant upper hand over those without any real-world project experience. So let us explore some of the most significant NLP project ideas to work on.
We will be discussing top natural language processing projects to become industry ready, solve real-life case studies impacting business and get hands-on with it. NLP mini projects with source code are also covered with their industry-wide applications contributing to the business.
Since the projects are end-to-end and provide a hands-on experience of trending technologies which makes you a clear winner in the job market, these can be considered ideal NLP projects for resumes. Here is the list of all the top NLP project ideas with source code:
If you are looking for ideas for NLP projects for Beginners and NLP projects for the Final year, we have got you covered! Here we provide the best NLP projects clearing all your basics and driving your knowledge into natural language processing project which is valued in the industry driving business: -
1. Extracting Important Keywords from Text with TF-IDF and Python's Scikit-Learn
Source: Geeksforgeeks
NLP projects is that of extracting keywords from text by applying the TF-IDF method which uses the ratio of frequency or count of terms in documents and frequency of terms in document collection which includes the number of documents. It is a fantastic lab providing the opportunity to work with text data preprocessing, and understanding document importance metrics. However, thanks to the use of python’s Scikit-Learn library it has become substantially easier to accomplish.
Here's a quick rundown of what I did:
That is a project in which I learned project evaluation before the utilization of term weighting in language analysis.
The project's aim is to extract interesting top keywords from the data text using TF-IDF and Python's SKLEARN library. The dataset taken is StackOverflow.
Here is the Source Code.
2. Chatbot with Seq2Seq Model
Source: Towards Data Science
Creating a chatbot from a Seq2Seq model was harder, but it was another project which has made me a better developer. Chatbots are ubiquitous, and building one made me see clearly how such AI is relevant.
Here's how I approached it:
This project uses a Seq2Seq model to build a straightforward talking chatbot. The TensorFlow library is used in the Python code. Here is the Source Code.
3. Language Identifier
The process of identifying the language of a particular text requires the use of multiple languages on a single page, the filtering through of numerous dialects, slang, and common terminology between languages. Machine learning greatly simplifies this procedure. You can create your language identifier using Facebook's fastText paradigm. The model uses word embeddings to understand a language and extends the word2vec tool. Source Code
4. Extract-Stock-Sentiment-From-News-Headlines
Financial news used to move slowly through radio, newspapers, and word-of-mouth over the course of days. It only takes a few seconds in the modern internet age. Did you know that data and streams from earnings calls are used to automatically generate news articles? By using sentiment analysis on financial news headlines from Finviz, we produce investing information in this project. We are able to decipher the sentiment behind the headlines and forecast whether the market is positive or negative about a stock by using this natural language processing technology. Source Code
We have what you need if you're seeking for Intermediate tasks! Here, we offer top natural language processing project ideas, which include the NLP areas that are most frequently utilized in projects and termed as interesting nlp projects.
1. Sentiment Analysis with Deep Learning using BERT
Source: Geeksforgeeks
An analysis of the grin annotations dataset using PyTorch Framework and large-scale language learnings from the pre-trained BERT transformer are used to build the sentiment analysis model. Multi-class classification is the purpose of the architecture. Loading of Tokenizers and additional data encoding is done during exploratory data analysis (EDA). Data loaders are made to make batch processing easier, and then Optimizer and Scheduler are set up to manage model training.
To regulate PyTorch's fine-tuning of BERT acceleration, a Training loop was created once the Performance measures for the model were developed. After being loaded, the pre-trained, fine-tuned model's performance was assessed, and it achieved good accuracy. Source Code
2. NLP-Topic-Modeling-LDA-NMF
NLP topic modeling that uses Latent Dirichlet Allocation(LDA) and Non-Negative Matrix Factorization(NMF) that I would consider to be very enlightening. This is the role they play in laying bare more themes, deeper contexts which are lying subtly within the sentences.
Here's a breakdown of my approach:
Here is the Source Code.
3. Speech Emotion Analyzer
The goal of this project is to develop a neural network model for recognizing emotions in the everyday talks we have. The male/female neural network model is capable of detecting up to five different emotions. This can be applied to marketing personalization to suggest products depending on emotions. Similarly, automakers can utilize this to gauge drivers' moods and change speed to prevent collisions. Source Code
4. Image captioning using LSTM
Source: Analytics Vidhya
The purpose of the picture captioning is to create a succinct and accurate explanation of the contents and context of an image. Applications for image captioning systems include automated picture analysis, content retrieval, and assistance for people with visual impairments.
Long Short-Term Memory (LSTM) is a form of Recurrent Neural Network (RNN) architecture that works well for applications like picture captioning that call for the modelling of long-term relationships in sequential input. A convolutional neural network (CNN) processes the input image in an image captioning system that uses LSTM in order to extract a fixed-length feature vector that represents the image. The LSTM network uses this feature vector as input to create the caption word by word. Source Code
If you're looking for Advanced tasks, we offer what you need! Here, we provide high-quality natural language processing example projects that incorporate the NLP domains that are most frequently used in projects and enable you to put your skills to work on a project that will be valued by the market. These are also called NLP advanced projects. Here:
1. Keyphrase extraction from scientific articles
The Natural Language Processing (NLP) task of key phrase extraction from scientific papers includes automatically finding and extracting significant words or terms from the texts.
There are many approaches for extracting key phrases, including rule-based methods, unsupervised methods, and supervised methods. Unsupervised methods employ statistical techniques to determine the terms that are most crucial in the document, while rule-based methods use a set of predefined criteria to select keyphrases. Source Code
2. Text classification with meta-learning
Source: Semantic Scholar
The use of machine learning models that are trained on several tasks and tailored for certain NLP tasks, such as sentiment analysis, text classification, and others, is what text classification using meta-learning entails. This method performs better than training models from scratch because it uses the knowledge learned from completing similar tasks to swiftly adapt to a new task. By adjusting the model's parameters using data from the support set, the objective is to reduce the loss on the query set. Source Code
3. Question answering with DistilBERT
The DistilBERT Model introduced the DistilBERT model. Introducing the paper DistilBERT, a distilled version of BERT that is smaller, quicker, cheaper, and lighter than the original BERT. DistilBERT is a BERT base-trained Transformer model that is compact, quick, affordable, and light. Compared to bert-base-uncased, it runs 60% faster and uses 40% less parameters while maintaining over 95% of BERT's performance on the GLUE language understanding benchmark. This model is a DistilBERT-base-uncased fine-tune checkpoint that was refined using (a second step of) knowledge distillation on SQuAD v1.1. Source Code
4. Masked word completion with BERT
Source: Analytics Vidhya
BERT is a transformers model that was self-supervisedly pretrained on a sizable corpus of English data. This means that an automatic process was used to generate inputs and labels from those texts after it had been pretrained on solely the raw texts without any human labelling (which explains why it may use a large amount of publically available data). The two learning goals for the model are Next Sentence Prediction (NSP) and Masked Language Modelling (MLM). A typical classifier can be trained using the features produced by the BERT model as inputs if you have a dataset of labelled sentences, for example. Here is the Source Code.
Here is some more NLP projects and their source code that you can work on to develop your skills.
| NLP Project Ideas | Source Code |
| 1. Autocorrect Feature Using NLTK In Python | Source Code |
| 2. Intent Recognition using TensorFlow | Source Code |
| 3. Machine Translation with Transformers | Source Code |
| 4. Hindi to English translation using RNN | Source Code |
| 5. Resume Parser using Python | Source Code |
| 6. Stock Price Prediction Project using TensorFlow | Source Code |
| 7. Time Series Forecasting using PyTorch | Source Code |
| 8. Amazon Product Review Sentiment Analysis using RNN | Source Code |
An open-source project must have its source code made publicly available so that it can be redistributed and updated by a group of developers. For the offered benefits of the platform and its users, open-source initiatives incorporate ideals of an engaged community, cooperation, and transparency.
Open-source NLP projects are booming and getting lot of attention from developers due to their ease of deployment, contributions from around the globe are done in the projects. Some of the trending NLP open-source projects are:
Rasa is an open-source machine learning platform for text- and voice-based conversations. You can create the contextual assistants mentioned above using Rasa. Rasa helps you create contextual assistants capable of producing rich, back-and-forth discussions. A contextual assistant must use context to produce items that have previously been provided to it in order to significantly replace a person.
Python2 and Python3 are both compatible with the text data processing module known as TextBlob. It puts into practice a straightforward API for handling common natural language processing (NLP) tasks. TextBlob is capable of completing a variety of tasks, such as classifying, translating, extracting noun phrases, sentiment analysis, and more.
3. Hugging Face
It is a sizable open-source community that creates tools to let users create, train, and use machine learning models based on open-source technology and code. Hugging Face's toolset makes it simple for other practitioners to exchange tools, models, model weights, datasets, etc. It is most renowned for its library of transformers.
Natural Language processing or NLP is a fast-expanding discipline, and there are several job prospects for experts who can create and implement NLP-based solutions and here you should work on it:
There are various platforms which offers opportunity to code NLP projects using pre-trained models, opportunity to fine-tune models, transfer learning etc. Platforms are:
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Although anyone can add "NLP proficiency" to their CV, not everyone can support it with a project that you can present to potential employers. We recommend getting hands-on ready with this Natural Language Processing with Python Training to explore NLP to the fullest.
Learning natural language processing (NLP) is a crucial ability for anyone who is interested in data science. There is a vast demand for qualified individuals in the growing field of NLP, which has a wide range of practical applications. A shrewd and practical approach is necessary for effective NLP learning. We recommend KnowldegeHut's Data Science course fees in India, offering top-notch content with projects.
The top 12 NLP project ideas that we covered can act as a jumping-off point for your NLP adventure. NLP beginner projects and nlp advanced projects are a great way to start your journey. You can maintain your knowledge and continue to develop your abilities by participating in online groups, going to conferences, and reading research articles.
We recommend starting NLP project involves clearing basics of it, learning a programming language and then implementing the core concepts of NLP in real-world projects.
Text summarization, machine translation, ticket classification are few examples of Natural Language Processing (NLP).
Natural language processing (NLP) comprises four elements:
An NLP project's ultimate objective is to develop a model or system that can handle natural language data in a way that is precise, effective, and practical for a given job or application. This may involve enhancing chatbot functionality, speech recognition, language translation, and a variety of other uses.
Yes, you can learn NLP on your own. But if you take a structured course designed by experts with doubt clearing sessions, then that will be beneficial as your doubts will not be a roadblock to your success!
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