Tutorial On Keras Tokenizer For Text Classification in NLP

Tutorial On Keras Tokenizer For Text Classification in NLP - exploring Keras tokenizer through which we will convert the texts into sequences. In this article, we will explore Keras tokenizer through which we will convert the texts into sequences that can be further fed to the predictive model. To do this we will make use of the Reuters data set that can be directly imported from the Keras library or can be downloaded from Kaggle. This data set contains 11,228 newswires from Reuters having 46 topics as labels. We will make use of different modes present in Keras tokenizer and will build deep neural networks for classification.

Natural language processing has many different applications like  Text Classification, Informal Retrieval, POS Tagging, etc. Almost all tasks in NLP, we need to deal with a large volume of texts. Since machines do not understand the text we need to transform it in a way that machine can interpret it. Therefore we convert texts in the form of vectors. There are many different methods to do this conversion like count vectorizer,  TF-IDF vectorizer, and also Keras have tokenizers that serve the same purpose.

In this article, we will explore Keras tokenizer through which we will convert the texts into sequences that can be further fed to the predictive model. To do this we will make use of the Reuters data set that can be directly imported from the Keras library or can be downloaded from  Kaggle. This data set contains 11,228 newswires from Reuters having 46 topics as labels. We will make use of different modes present in Keras tokenizer and will build  deep neural networks for  classification.

What we will learn from this article?

How to use Keras Tokenizer?

What are different modes in Keras Tokenizer? 

How to build classification models over the Reuters data set? 

Model Performance for Different Modes Of Tokenization

developers corner keras tokenizer nlp text classification

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