![]() Now each of these problems has something in common. Find toxic comments on a platform like Facebook.Text classification is a common task in natural language processing, which transforms a sequence of a text of indefinite length into a category of text. So first let me start with explaining a little more about the text classification problem. , which talks about various deep learning models in use in NLP and What Kagglers are using for Text Classification Till then you can take a look at my other posts: It might take me a little time to write the whole series. This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. You can start for free with the 7-day Free Trial. Natural Language Processing Specialization We will try to use various other models which we were not able to use in this competition like ULMFit transfer learning approaches in the fourth post in the series.Īs a side note: If you want to know more about NLP, I would like to recommend this awesome We will delve deeper into Deep learning models in the third post which will focus on different architectures for solving the text classification problem. ![]() that have been used in text classification and try to access their performance to create a baseline. , I will try to take you through some basic conventional models like TFIDF, Count Vectorizer, Hashing etc. this one will be based on preprocessing techniques that work with Deep learning models and we will also talk about increasing embeddings coverage. ![]() Since we have a large amount of material to cover, I am splitting this post into a series of posts. It is an NLP Challenge on text classification and as the problem has become more clear after working through the competition as well as by going through the invaluable kernels put up by the kaggle experts, I thought of sharing the knowledge. Recently, I started up with an NLP competition on Kaggle called Quora Question insincerity challenge.
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