Addressing synonyms in Supervised Learning for Text Classification
<p>I am using scikit-learn supervised learning method for text classification. I have a training dataset with input text fields and the categories they belong to. I use tf-idf, SVM classifier pipeline for creating the model. The solution works well for normal testcases. But if a new text is entered which has synoynmous words as in the training set, the solution fails to classify correctly. 
For e.g: the word 'run' might be there in the training data but if I use the word 'sprint' to test, the solution fails to classify correctly. </p>

<p>What is the best approach here? Adding all synonyms for all words in training dataset doesn't look like a scalable approach to me</p>