What is text normalization?
I am learning natural language processing (NLP). Recently I encountered the term "text normalization". Can somebody explain me, please, what is text normalization in NLP?
I am learning natural language processing (NLP). Recently I encountered the term "text normalization". Can somebody explain me, please, what is text normalization in NLP?
There can be several definitions. What was the context where you learn about text normalization?
I have read an article about how to improve the quality of text-to-sound conversion. They stated that text normalization is an important element of the improvement of the results.
It is still not vivid enough. I know that text normalization is the process of unfolding text before feeding it to a machine learning model. But there can be other interpretations.
Can you please tell more about text unfolding?
Yes. You may notice that texts often include many special characters, abbreviations, numbers, etc. And, for example, numbers can be pronounced differently depends on the context. It is especially actual for languages like Russian, where words can be modified by tense, number, gender, etc. It is not very hard for a human to understand how to pronounce such abbreviations. But for machine learning models it is a hard task. That's why we need to unfold all these abbreviations before doing machine learning. This is called text normalization.
Yes, probably this is what I have read about in that article. Thank you!
Wkniuz48 But what about other interpretations of this notion? I'm curious about them. Explain them too, please.
Probably text normalization in NLP could be the processing of vectorized texts. As you may now, before feeding data to machine learning model you can preprocess it, perform feature engineering, etc. Maybe text normalization is the process where you remove stopwords from the text, perform stemming, lemmatization, framing, etc. All these text processing steps can be called text normalization also.
It really makes sense. Thanks for your explanation!
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