Python Natural Language Processing

Challenges of features engineering

Here, we will discuss the challenges of features engineering for NLP applications. You must be thinking that we have a lot of options available in terms of tools and algorithms, so what is the most challenging part? Let's find out:

• In the NLP domain, you can easily derive the features that are categorical features or basic NLP features. We have to convert these features into a numerical format. This is the most challenging part.
• An effective way of converting text data into a numerical format is quite challenging. Here, the trial and error method may help you.
• Although there are a couple of techniques that you can use, such as TF-IDF, one-hot encoding, ranking, co-occurrence matrix, word embedding, Word2Vec, ...