Artificial Intelligence has multiple branches, of which Natural language processing (NLP) has emerged as a powerful new-age tool. NLP goes back to the 1950s when Alan Turing released an article – “Computing Machinery and Intelligence” – that proposed a test (now known as the Turing test) involving automated interpretation and generation of natural human languages. Even so, NLP has only recently gained global recognition and popularity.
Table of Contents
What is Natural Language Processing?
Natural Language Processing is all about facilitating human-to-machine communications. It aims to train computers to understand, interpret, and manipulate natural human languages. NLP draws inspiration from multiple disciplines such as Artificial Intelligence, Computer Science, and Computational Linguistics.
Humans communicate in their native languages such as English, Japanese, Spanish, etc., whereas computers speak in their native language, which is binary language. While computers cannot understand our natural human languages, machine language is mostly incomprehensible to most people.
This is where NLP enters to bridge the gap between human communication and computer understanding. Natural language processing empowers and trains computers to communicate with humans in their native language by helping them read texts, hear speech, interpret audio/text messages, measure sentiment, and much more.
With the explosion of data brought about by the everyday interactions and transactions in the digital world, natural language processing has become more crucial for businesses. Thanks to NLP, companies can harness vast volumes of raw business data, social media chatter, etc., to make sense of data and take data-oriented decisions.
In this article, we will list 12 NLP projects on GitHub to inspire you! Working on these projects will help enrich your domain knowledge and sharpen your real-world skills.
Top GitHub NLP Projects
Paraphrase detection is an NLP application that detects whether or not two different sentences have the same meaning. It is widely used in machine translation, question answering, information extraction/retrieval, text summarization, and natural language generation.
This is a beginner-friendly project wherein you will build a paraphrase identification system that can accurately identify the similarities and differences between two textual entities (for example, sentences) by applying syntactic and semantic analyses on them.
This is another beginner-friendly project that aims to quantify the similarities between two documents by using the Cosine similarity method. By finding the similarities between the two papers, this project will highlight the common topics of discussion.
Cosine similarity converts two documents to vectors to compute the similarity between those vectors. It calculates the document similarities by taking the inner product space that measures the cosine angle between them.
In this project, you’ll build an application that can predict the next word as you type words. The tools used to create this text prediction project include Natural Language Processing, Text Mining, and R’s suite of tools.
The project uses a Maximum Likelihood estimator with Kneser Ney Smoothing as the prediction model. The prediction is designed on the collection of words stored in the database used for training the model. You can find the complete set of resources for this project on GitHub.
This project is a part of the Science of Success project. The aim here is to determine if specific lexical factors can indicate the attention an article received, as measured by normalized citation indices, using a host of data science and NLP analytical tools.
In the initial phases, this project focuses on studying the temporal and disciplinary variance in the length and syntactic features of article titles in the Web of Science – a dataset containing over 50 million articles published since 1900. The bigger picture is to create a quantitative model that can accurately estimate a scientific paper’s impact on the community.
As the title suggests, you will use sentiment analysis on financial news headlines from Finviz to produce investing insights in this project. The sentiment analysis technique will help you understand and interpret the emotion behind the headlines and predict whether the present market situation is in favor of a particular stock or not.
This project involves building a smart bot that can parse and match results from a specific repository to answer questions. The bot uses WordNet for this operation. It weighs the context of a question concerning the tags in structured documents (such as headers, bold titles, etc.). Since it retains the context, you can ask related questions around the same topic.
For instance, if you wish to query a Wikipedia article, you can use the template “Tell me about XYZ” and continue to ask similar questions once the context is established. Again, you can query a webpage by mentioning the page’s URL as the source like “https://www.microsoft.com/en-us/software-download/faq.” This works exceptionally well with FAQ and Q&A pages.
The CitesCyVerse project is designed on The Science Citation Knowledge Extractor. CitesCyVerse is an open-source tool that leverages Machine Learning and NLP to help biomedical researchers understand how others use their work by analyzing the content in articles that cite them. By using ML and NLP, CitesCyVerse extracts the prominent themes and concepts discussed in the citing documents. This enables researchers to better understand how their work influences others in the scientific community.
CitesCyVerse includes WordClouds that generates new clouds from similar words mentioned in citing papers. Also, it has Topics that lets you explore popular topics for articles and publications citing CyVerse.
In this Data Science capstone project, you will use data processing scripts to demonstrate data engineering instead of creating an n-gram model. These scripts can process the whole corpus to produce the n-grams and their counts. You can use this data to develop predictive text algorithms.
To build this project, you will need a dual-core system (since most scripts are single-threaded) with at least 16GB RAM. As for the software requirements, you need – Linux (best if tested on Ubuntu 14.04), Python (version 2.7), NLTK (version 3.0), and NumPy.
This is an exciting project where you’ll build RNNs to generate TV scripts for the popular show The Simpsons based on a script dataset of all the show’s 27 seasons. The RNNs will generate a new script for a specific scene shot at Moe’s Tavern.
The script generator project is a part of Udacity’s Deep Learning Nanodegree. The project implementation is contained in: dlnd_tv_script_generation.ipynb
This project seeks to understand how social media posts impact the future prices of individual stocks. Here, we’ll study the impact of social media posts on Reddit, particularly investment focused subreddits/forums, using text analysis methods.
You can use the GitHub repository files to clean and apply sentiment analysis to Reddit posts/comments and use this data to create regression models. The repository also includes the code that you can use for the interactive web application utilized for visualizing real-time sentiment for specific stocks tickers and make relevant predictions.
This is a fun NLP project where you will develop a bot named Me_Bot that will leverage your WhatsApp conversations, learn from them, and converse with you just as you would with another person. Essentially, the idea is to create a bot that speaks like you.
You need to export your WhatsApp chats from your phone and train the bot on this data. To do so, you have to go to WhatsApp on your phone, choose any conversation, and export it from the app’s settings. Then you can shift the “.txt” file generated to the Me_Bot folder.
This project revolves around creating an ML model that can detect emotions from the conversations we have commonly in our daily life. The ML model can detect up to five different emotions and offer personalized recommendations based on your present mood.
This emotion-based recommendation engine is of immense value to many industries as they can use it to sell to highly targeted audience and buyer personas. For instance, online content streaming platforms can use this tool to offer customized content suggestions to individuals by reading their current mood and preference.
Also Read: Deep Learning vs NLP
With that, we have reached the end of our list. These 12 NLP projects on GitHub are excellent for honing your coding and project development skills. Most importantly, project building will help you master the nuances of Natural Language Processing, thereby strengthening your domain knowledge.
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, check out IIIT-B & upGrad’s PG Diploma 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.
What are the main challenges of Natural language processing?
Natural language processing has a lot of challenges. The major problem is the lack of availability of computational power. The current algorithms are created to run in off-line systems which need huge computational power and may take more time to complete the processing. The other problem is the available resources. Creating an algorithm that can work with a small amount of data is not easy and more time consuming. Another challenge is the availability of huge amounts of data that we need to process.
Which NLP model gives the best accuracy?
The best accuracy for NLP models is achieved by passing the text through a series of increasingly sophisticated filters. The first layer is to remove stop words, punctuation, and numbers. After that, the entire text should be stemmed using a Porter stemmer, then all of the words should be replaced by their lemmatized forms. Then, the final step is to remove any words that do not exist in a vocabulary of 200,000 words.
What is tokenization in NLP?
Tokenization is a process of breaking down a sentence into its constituent parts, called tokens. After applying the process, we can easily extract the meaning or intent of a sentence. Tokenization is done after doing sentence splitting. In NLP, the tokens are used for further processing, classification and representation of the sentence. Some of the NLP tasks that involve tokenization are language detection, POS tagging and parsing.