![]() ![]() For example, in English, adjectives are more commonly positioned before the noun (red flower, bright candle, colorless green ideas) verbs are words that denote actions and which have to exist in a phrase (for it to be a phrase)… However, inside one language, there are commonly accepted rules about what is “correct” and what is not. ![]() Grammars! The word always reminds me of bulky ancient books. Of course, we follow cultural conventions learned from childhood, which may vary a little depending on region or background (you might have noticed, for example, that I use a somewhat ‘weird’ style in my phrasing - that’s because even though I’ve read and learned some english, portuguese is still my mother language and the language that I think in). Have you ever stopped to think how we structure phrases? They are not random choices of words - you actually follow a structure when reasoning to make your phrase. To start, let us analyze a little about sentence composition. However, I’ll try to keep it understandable as promised, so don’t worry if you don’t know what is a Supervised Machine Learning Model, or if you have doubts about what is a Tree Bank, since I’ll try to make it as clear and simple as possible. In this article, we’ll use some more advanced topics, such as Machine Learning algorithms and some stuff about grammar and syntax. The idea is to be able to extract “hidden” information from our text and also enable future use of Lemmatization, a text normalization tool that depends on PoS tags for correction. In this article, following the series on NLP, we’ll understand and create a Part of Speech (PoS) Tagger. Time to dive a little deeper onto grammar. ![]()
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