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Natural Language Processing (NLP) Projects & Topics For Beginners [2023]

What are Natural Language Processing Projects?

NLP project ideas advanced encompass various applications and research areas that leverage computational techniques to understand, manipulate, and generate human language. These projects harness the power of artificial intelligence and machine learning to process and analyze textual data in ways that mimic human understanding and communication. Here are some key aspects and examples of NLP projects:

1. Text Classification

NLP can be used to classify text documents into predefined categories automatically. This is useful in sentiment analysis, spam detection, and topic categorization. For instance, classifying customer reviews as positive or negative to gauge product sentiment.

It also plays a crucial role in topic categorization, aiding in the organization and understanding of large volumes of textual data. Natural Language Processing projects help us understand Text Classification better by letting us put theories into action. 

Through hands-on projects, we get to apply text classification algorithms to real situations, like figuring out if customer reviews are positive or negative. These projects expose us to different types of text data challenges, such as messy information or imbalanced categories, helping us learn how to handle them.

2. Named Entity Recognition (NER)

Named Entity Recognition (NER) is a vital part of Natural Language Processing (NLP) that helps machines identify and categorize specific entities in a given text.

NLP models can identify and categorize entities such as names of people, organizations, locations, and dates within text. This is crucial for information extraction tasks like news article analysis or document summarization.

Natural Language Processing projects focusing on Named Entity Recognition provide hands-on experience with extracting valuable information from unstructured text. For instance, when analyzing news articles, NER can be applied to pinpoint key entities, making it easier to understand the main players, locations, and dates involved in a story.

Projects in NLP also allow practitioners to explore practical applications of NER beyond its standalone use. For instance, integrating NER into larger projects like document summarization or information extraction showcases its versatility and relevance in solving complex NLP challenges.

3. Machine Translation

Projects in this domain focus on developing algorithms that translate text from one language to another. Prominent examples include Google Translate and neural machine translation models.

The goal of machine translation is to enable seamless communication between people who speak different languages, breaking down language barriers and fostering global understanding. MT systems require extensive training data in multiple languages to learn the patterns and nuances of language pairs. Projects in NLP involve sourcing and preprocessing large bilingual corpora, including translated texts, to train robust translation models.

Natural Language Processing projects in machine translation provide a practical understanding of the technical, linguistic, and ethical dimensions involved in building effective translation models, contributing to the ongoing efforts to facilitate cross-language communication in diverse contexts.

4. Text Generation

Text generation is a fascinating aspect of Natural Language Processing (NLP) that involves creating coherent and contextually relevant text automatically using computer algorithms. These algorithms can range from traditional rule-based methods to more advanced deep learning models.

NLP models like GPT-3 can generate human-like text, making them useful for content generation, chatbots, and creative writing applications.

The goal of text generation is to produce human-like text that follows the style and structure of a given language. In NLP based projects, text generation often explores conditional scenarios, where the output is influenced by specific input conditions, making it applicable for tasks like chatbot responses or context-based sentence completion. Data preprocessing plays a pivotal role in preparing diverse and representative datasets for effective model training. 

5. Question-Answering Systems

Question-Answering (QA) Systems represent a significant area within Natural Language Processing that focuses on developing algorithms capable of comprehending questions posed in natural language and providing relevant and accurate answers. These systems aim to bridge the gap between human language understanding and machine processing, allowing users to interact with computers in a more conversational manner.

These nlp project ideas involve building systems that can understand questions posed in natural language and provide relevant answers. IBM’s Watson is a well-known example.

NLP project ideas based on QA systems may also explore context-aware systems, where the model considers the broader context of a conversation or passage to provide more accurate answers.

6. Speech Recognition

While technically part of the broader field of speech processing, NLP techniques are used in transcribing spoken language into written text, as seen in applications like voice assistants (e.g., Siri and Alexa).

These NLP related projects involve the collection of high-quality audio datasets with diverse speakers and linguistic variations that are essential for training robust models. Preprocessing steps involve converting audio signals into a format suitable for analysis, often using techniques like spectrogram representations.

NLP projects in Python have diverse applications especially when it comes to speech recognition. They range from the development of voice assistants and dictation software to transcription services and voice-controlled devices. The outcomes contribute significantly to the creation of hands-free interfaces, facilitating accessibility features for differently-abled individuals, and propelling advancements in voice-activated technologies.

All in all, there are many easy NLP projects in Speech Recognition that beginners can take up to develop a deeper understanding of spoken language by computers, enhancing human-computer interaction intuitively and expanding accessibility across various applications and user scenarios.

7. Text Summarization

NLP can automatically generate concise summaries of lengthy texts, making it easier to digest information from news articles, research papers, or legal documents.

NLP based projects in Text Summarization explore different techniques, such as extractive summarization, where the algorithm selects and combines existing sentences, and abstractive summarization, where it generates new sentences to convey the essential meaning.

The applications of Text Summarization projects are diverse and impactful. They are used to quickly condense lengthy articles, news, or documents, providing readers with a concise version that captures the main ideas. These projects in NLP essentially empower computers to act as efficient summarizers, making information more accessible and saving time for users who need a quick understanding of complex texts.

8. Sentiment Analysis

Analyzing social media data and customer reviews to determine public sentiment toward products, services, or political issues is a common NLP application.

NLP project ideas focusing on Sentiment Analysis, algorithms are trained to analyze words and phrases to determine the overall sentiment conveyed by a piece of text.

These projects are particularly useful in various applications, such as assessing customer reviews, monitoring social media sentiments, or gauging public opinion. The goal is to help businesses and organizations understand how people feel about their products, services, or specific topics.

9. Language Modeling

Language Modeling is a fundamental concept in Natural Language Processing (NLP) that involves teaching computers to understand and predict the structure and patterns of human language

Creating and fine-tuning language models, such as BERT and GPT, for various downstream tasks forms the core of many NLP projects. These models learn to represent and understand language in a generalized manner.

Language Modeling projects in NLP play a pivotal role in enabling computers to grasp the intricacies of human language, facilitating applications that require language understanding and generation. These projects are essential in various NLP applications, such as speech recognition, machine translation, and text generation. By understanding the structure of language, computers can generate coherent and contextually relevant text, making interactions with machines more natural and human-like.

What are the Different Best Platforms to Work on Natural Language Processing Projects?

Here are some of the best platforms for nlp projects for final year:

1. Python and Libraries

Python is the most popular programming language for NLP due to its extensive libraries and frameworks. Its user-friendly syntax and readability also make it particularly suitable for students with varying programming experience. Therefore, it stands out as an excellent platform to undertake NLP projects for final year students.

Libraries like NLTK, spaCy, gensim, and the Transformers library by Hugging Face provide essential NLP functionalities and pre-trained models. In addition, visualization tools like Matplotlib and Seaborn contribute to effective project presentation. Collectively, the combination of Python and its libraries provides a conducive and resource-rich environment for successful Natural Language Processing with Python projects.

2. TensorFlow and PyTorch

These deep learning frameworks provide powerful tools for building and training neural network models, including NLP models. Researchers and developers can choose between them based on their preferences.

They are powerful tools to aid in building smart computer systems, especially for final year students working on NLP related projects. TensorFlow, made by Google, is known for being flexible and great for big projects on machine learning and deep learning. On the other hand, PyTorch’s dynamic graph is well-suited for research-oriented work. Both frameworks have rich documentation, and active communities, and support a variety of model architectures.

3. Google Colab

For cloud-based NLP development, Google Colab offers free access to GPU and TPU resources, making it an excellent choice for training large NLP models without needing high-end hardware.

It serves more like a cloud-based platform, offering free access to GPUs and TPUs. It’s akin to a virtual workspace where users can run code, train models, and analyze data without the constraints of computational resources. Its integration with popular libraries like TensorFlow and PyTorch makes it an excellent choice for collaborative and resource-intensive Natural Language Processing projects.

4. SpaCy

SpaCy is a fast and efficient NLP library that excels at various NLP tasks, including tokenization, named entity recognition, and part-of-speech tagging. It also offers pre-trained models for multiple languages. SpaCy functions as a language expert in projects involving extensive text data. Its reputation for speed and efficiency makes it a preferred tool for NLP projects for beginners.

5. Docker

Docker containers can create reproducible and portable NLP environments, ensuring consistency across development and deployment stages.

It acts as a versatile containerization tool, allowing users to package an entire project, along with its dependencies, into a single, reproducible unit. This is particularly advantageous for NLP projects with specific software configurations, ensuring consistency across different environments. Docker addresses the common challenge of project reproducibility.

6. AWS, Azure, and Google Cloud

These cloud platforms offer scalable compute resources and specialized NLP services like Amazon Comprehend, Azure Text Analytics, and Google Cloud NLP, simplifying the deployment of NLP solutions at scale.

These platforms are like powerful virtual data centers offering computing power services to storage and machine learning tools. AWS is known for its extensive service offerings, Azure seamlessly integrates with Microsoft technologies, and Google Cloud excels in data analytics and machine learning. For students taking up both NLP mini project topics and big project topics, these platforms provide access to cutting-edge technologies without the need for substantial hardware investments.

7. Kaggle

Kaggle provides datasets, competitions, and a collaborative platform for NLP practitioners to share code and insights. It’s a great resource for learning and benchmarking NLP models.

Like a virtual playground for data scientists, Kaggle provides datasets for analysis, hosts machine learning competitions, and allows users to create and share code through Jupyter notebooks. For students working on NLP projects, it is a collaborative space where they can apply their data science skills in real-world scenarios, learn from others, and build a portfolio that demonstrates their capabilities to potential employers.

8. GitHub

GitHub is a repository for NLP project code, facilitating collaboration and version control. Many NLP libraries and models are open-source and hosted on GitHub.

Students can host their code repositories on GitHub, track changes, and collaborate with peers.

It’s an invaluable tool for final-year projects, facilitating version management, issue tracking, and showcasing their works in natural language processing projects for GitHub to prospective employers.

9. Apache Spark

Apache Spark can be used for handling large-scale NLP tasks for distributed data processing and machine learning. Apache Spark is an open-source framework for big data processing, handling tasks like batch processing, streaming, machine learning, and graph processing efficiently. With its in-memory processing and support for multiple languages, it’s a versatile tool for final-year projects dealing with large datasets or complex computations, making tasks scalable and faster.

NLP Projects & Topics

Natural Language Processing or NLP is an AI component concerned with the interaction between human language and computers. When you are a beginner in the field of software development, it can be tricky to find NLP based projects that match your learning needs. So, we have collated some examples to get you started. So, if you are a ML beginner, the best thing you can do is work on some NLP projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting NLP projects which beginners can work on to put their knowledge to test. In this article, you will find top NLP project ideas for beginners to get hands-on experience on NLP.

But first, let’s address the more pertinent question that must be lurking in your mind: why to build NLP projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience.

NLP is all about analyzing and representing human language computationally. It equips computers to respond using context clues just like a human would. Some everyday applications of NLP around us include spell check, autocomplete, spam filters, voice text messaging, and virtual assistants like Alexa, Siri, etc. As you start working on NLP projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

In the last few years, NLP has garnered considerable attention across industries. And the rise of technologies like text and speech recognition, sentiment analysis, and machine-to-human communications, has inspired several innovations. Research suggests that the global NLP market will hit US$ 28.6 billion in market value in 2026. 

When it comes to building real-life applications, knowledge of machine learning basics is crucial. However, it is not essential to have an intensive background in mathematics or theoretical computer science. With a project-based approach, you can develop and train your models even without technical credentials. Learn more about NLP Applications.

To help you in this journey, we have compiled a list of NLP project ideas, which are inspired by actual software products sold by companies. You can use these resources to brush up your ML fundamentals, understand their applications, and pick up new skills during the implementation stage. The more you experiment with different NLP projects, the more knowledge you gain.

Before we dive into our lineup of NLP projects, let us first note the explanatory structure. 

The project implementation plan

All the nlp projects for final year included in this article will have a similar architecture, which is given below:

  • Implementing a pre-trained model
  • Deploying the model as an API
  • Connecting the API to your main application

This pattern is known as real-time inference and brings in multiple benefits to your NLP design. Firstly, it offloads your main application to a server that is built explicitly for ML models. So, it makes the computation process less cumbersome. Next, it lets you incorporate predictions via an API. And finally, it enables you to deploy the APIs and automate the entire infrastructure by using open-source tools, such as Cortex. 

Here is a summary of how you can deploy machine learning models with Cortex:

  • Write a Python script to serve up predictions.
  • Write a configuration file to define your deployment.
  • Run ‘cortex deploys’ from your command line.

Now that we have given you the outline let us move on to our list! 

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So, here are a few NLP Projects which beginners can work on:

NLP Project Ideas

This list of NLP projects for students is suited for beginners, intermediates & experts. These NLP projects will get you going with all the practicalities you need to succeed in your career.

Further, if you’re looking for NLP based projects for final year, this list should get you going. So, without further ado, let’s jump straight into some NLP projects that will strengthen your base and allow you to climb up the ladder. This list is also great for Natural Language Processing projects in Python

Here are some NLP project idea that should help you take a step forward in the right direction.

1. A customer support bot

One of the best ideas to start experimenting you hands-on projects on nlp for students is working on customer support bot. A conventional chatbot answers basic customer queries and routine requests with canned responses. But these bots cannot recognize more nuanced questions. So, support bots are now equipped with artificial intelligence and machine learning technologies to overcome these limitations. In addition to understanding and comparing user inputs, they can generate answers to questions on their own without pre-written responses. 

For example, Reply.ai has built a custom ML-powered bot to provide customer support. According to the company, an average organization can take care of almost 40 % of its inbound support requests with their tool. Now, let us describe the model required to implement a project inspired by this product. 

You can use Microsoft’s DialoGPT, which is a pre-trained dialogue response generation model. It extends the systems of PyTorch Transformers (from Hugging Face) and GPT-2 (from OpenAI) to return answers to the text queries entered. You can run an entire DialoGPT deployment with Cortex. There are several repositories available online for you to clone. Once you have deployed the API, connect it to your front-end UI, and enhance your customer service efficiency!

Read: How to make chatbot in Python?

2. A language identifier

Have you noticed that Google Chrome can detect which language in which a web page is written? It can do so by using a language identifier based on a neural network model. 

This is an excellent nlp project in python for beginners. The process of determining the language of a particular body of text involves rummaging through different dialects, slangs, common words between different languages, and the use of multiple languages in one page. But with machine learning, this task becomes a lot simpler.

You can construct your own language identifier with the fastText model by Facebook. The model is an extension of the word2vec tool and uses word embeddings to understand a language. Here, word vectors allow you to map a word based on its semantics — for instance, upon subtracting the vector for “male” from the vector for “king” and adding the vector for “female,” you will end up with the vector for “queen.”

A distinctive characteristic of fastText is that it can understand obscure words by breaking them down into n-grams. When it is given an unfamiliar word, it analyzes the smaller n-grams, or the familiar roots present within it to find the meaning. Deploying fastTExt as an API is quite straightforward, especially when you can take help from online repositories.

3. An ML-powered autocomplete feature

Autocomplete typically functions via the key value lookup, wherein the incomplete terms entered by the user are compared to a dictionary to suggest possible options of words. This feature can be taken up a notch with machine learning by predicting the next words or phrases in your message.

Here, the model will be trained on user inputs instead of referencing a static dictionary. A prime example of an ML-based autocomplete is Gmail’s ‘Smart Reply’ option, which generates relevant replies to your emails. Now, let us see how you can build such a feature. 

For this advanced nlp projects, you can use the RoBERTa language model. It was introduced at Facebook by improving Google’s BERT technique. Its training methodology and computing power outperform other models in many NLP metrics.

To receive your prediction using this model, you would first need to load a pre-trained RoBERTa through PyTorch Hub. Then, use the built-in method of fill_mask(), which would let you pass in a string and guide your direction to where RoBERTa would predict the next word or phrase. After this, you can deploy RoBERTa as an API and write a front-end function to query your model with user input. Mentioning NLP projects can help your resume look much more interesting than others.

4. A predictive text generator

This is one of the interesting NLP projects. Have you ever heard of the game AI Dungeon 2? It is a classic example of a text adventure game built using the GPT-2 prediction model. The game is trained on an archive of interactive fiction and demonstrates the wonders of auto-generated text by coming up with open-ended storylines. Although machine learning in the area of game development is still at a nascent stage, it is set to transform experiences in the near future. Learn how python performs in game development.

DeepTabNine serves as another example of auto-generated text. It is an ML-powered coding autocomplete for a variety of programming languages. You can install it as an add-on to use within your IDE and benefit from fast and accurate code suggestions. Let us see how you can create your own version of this NLP tool. 

You should go for Open AI’s GPT-2 model for this project. It is particularly easy to implement a full pre-trained model and to interact with it thereafter. You can refer to online tutorials to deploy it using the Cortex platform. And this is the perfect idea for your next NLP project!

Read: Machine Learning Project Ideas

5. A media monitor

One of the best ideas to start experimenting you hands-on NLP projects for students is working on media monitor. In the modern business environment, user opinion is a crucial denominator of your brand’s success. Customers can openly share how they feel about your products on social media and other digital platforms. Therefore, today’s businesses want to track online mentions of their brand. The most significant fillip to these monitoring efforts has come from the use of machine learning. 

For example, the analytics platform Keyhole can filter all the posts in your social media stream and provide you with a sentiment timeline that displays the positive, neutral, or negative opinion. Similarly, an ML-backed sift through news sites. Take the case of the financial sector where organizations can apply NLP to gauge the sentiment about their company from digital news sources. 

Such media analytics can also improve customer service. For example, providers of financial services can monitor and gain insights from relevant news events (such as oil spills) to assist clients who have holdings in that industry. 

You can follow these steps to execute a project on this topic: 

  • Use the SequenceTagger framework from the Flair library. (Flair is an open-source repository built on PyTorch that excels in dealing with Named Entity Recognition problems.)
  • Use Cortex’s Predictor API to implement Flair.

We are currently experiencing an exponential increase in data from the internet, personal devices, and social media. And with the rising business need for harnessing value from this largely unstructured data, the use of NLP instruments will dominate the industry in the coming years.

Such developments will also jumpstart the momentum for innovations and breakthroughs, which will impact not only the big players but also influence small businesses to introduce workarounds. 

Also read: AI Project Ideas and Topics for Beginners

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Natural Language Processing Techniques to Use in Python

Making computers read unorganized texts and extract useful information from them is the aim of natural language processing (NLP). Many NLP approaches can be implemented using a few lines of Python code, courtesy of accessible libraries like NLTK, and spaCy. These approaches can also work great as NLP topics for presentation

Here are some techniques of Natural Language Processing projects in Python – 

  • Named Entity Recognition or NER – A technique called named entity recognition is used to find and categorise named entities in text into groups like people, organisations, places, expressions of times, amounts, percentages, etc. It is used to improve content classification, customer service, recommendation systems, and search engine algorithms, among other things.
  • Analysis of Sentiment – One of the most well-known NLP approaches, sentiment analysis examines text (such as comments, reviews, or documents) to identify whether the information is good, poor, or indifferent. Numerous industries, including banking, healthcare, and customer service, can use it.
  • BoW or Bag of Words – A format that transforms text into stationary variables is called the Bag of Words (BoW) model. This makes it easier for us to convert text to numbers to be used in machine learning. The model is simply interested in the number of terms in the text and isn’t focused on word order. It may be used for document categorisation, information retrieval, and NLP. Cleaning raw text, tokenisation, constructing a vocabulary, and creating vectors are all steps in the normal BoW approach.
  • TF-IDF (Term Frequency – Inverse Document Frequency) – The TF-IDF calculates “weights” that describe how significant a word is in the document.  The quantity of documents that include a term reduces the TF-IDF value, which rises according to the frequency of its use in the document. Simply said, the phrase is rare, more distinctive, or more important the higher the TF-IDF score, and vice versa. It has uses in information retrieval, similar to how browsers try to yield results that are most pertinent to your request. 

TF and IDF are calculated in different ways. 

TF = (Number of duplicate words in a document) / (Number of words in a document)

IDF = Log {(Number of documents) / (Number of documents with the word)}

  • Wordcloud – A common method for locating keywords in a document is word clouds. In a Wordcloud, words that are used more frequently have larger, stronger fonts, while those that are used less frequently have smaller, thinner fonts. With the ‘Wordcloud’ library and the ‘stylecloud’ module, you can create simplistic Wordclouds in Python. This makes NLP projects in Python very successful. 

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NLP Research Topics 

To ace NLP projects in Python, it is necessary to conduct thorough research. Here are some NLP research topics that will help you in your thesis and also work great as NLP topics for presentation – 

  • Biomedical Text Mining
  • Computer Vision and also NLP
  • Deep Linguistic Processing
  • Controlled Natural Language
  • Language Resources and also Architectures for NLP
  • Sentiment Analysis and also Opinion Mining
  • NLP includes Artificial Intelligence
  • Issues includes Natural language understanding and also Creation
  • Extraction of Actionable Intelligence also from Social Media
  • Efficient Information also Extraction Techniques
  • Use of Rule also based Approach or Statistical Approach
  • Topic Modelling in Web data

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Conclusion

In this article, we covered some NLP projects that will help you implement ML models with rudimentary knowledge software development. We also discussed the real-world applicability and functionality of these products. So, use these topics as reference points to hone your practical skills and propel your career and business forward! 

Only by working with tools and practise can you understand how infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our NLP projects guide to build your very own NLP projects!

If you wish to improve your NLP skills, you need to get your hands on these NLP projects. If you’re interested to learn more about machine learning online course, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

How easy it is to implement these projects?

These projects are very basic, someone with a good knowledge of NLP can easily manage to pick and finish any of these projects.

Can I do this projects on ML Internship?

Yes, as mentioned, these project ideas are basically for Students or Beginners. There is a high possibility that you get to work on any of these project ideas during your internship.

Why do we need to build NLP projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience.

What is natural language processing?

Natural language processing (NLP) is a subject of computer science—specifically, a branch of artificial intelligence (AI)—concerning the ability of computers to comprehend text and spoken words in the same manner that humans can. Computational linguistics—rule-based human language modeling—is combined with statistical, learning algorithms, and deep learning models.

How to implement any NLP project?

The design of all the projects will be the same: Implementing a pre-trained model, deploying the model as an API, and connecting the API to your primary application. Real-time inference is a pattern that delivers several benefits to your NLP design. To begin with, it offloads your core application to a server designed specifically for machine learning models. As a result, the computation procedure is simplified. Then, using an API, you may incorporate predictions. Finally, it allows you to use open-source tools like Cortex to install APIs and automate the entire architecture.

How to construct a language identifier?

This is a fantastic NLP project for newcomers. The method of identifying the language of a body of text entails combing through many dialects, slangs, cross-language common terms, and the use of numerous languages on a single page. This task, however, becomes a lot easier with machine learning. With Facebook's fastText concept, you can create your own language identifier. The model employs word embeddings to comprehend a language and is an expansion of the word2vec tool. Word vectors enable you to map a word based on its semantics — for example, you can get the vector for Queen by subtracting the vector for Male from the vector for King and adding the vector for Female.

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