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Even though everyone else is doing it, we think it's worth it to show the practical applications of applying machine learning to taxonomy. And we think taxonomy is the hardest problem in data science. While taxonomy is often used to describe metadata on specific digital objects like books, movies, etc., often problems become much harder when focusing on multiple entities like objects, authors, publishers, languages and publisher's regional variations. That is where we can apply our skills to problems involving heterogeneous data, including text and code.
We have built a machine learning based solution for taxonomy that focuses on the unlabeled data aspects of the problem. The solution is not limited by the structure of the data. The entities of interest can be:
Books
Games
Products
Languages
Publishers
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These are generally multi-class classification and regression problems. Our research involves the use of Neural Networks and Deep Learning techniques.
We have built a Web application, Taku, that allows anyone to try out our solution. The application will not only allow them to easily apply our algorithm to their dataset, but also showcase the results in a way that is easily understandable 0b46394aab
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