![]() ![]() Here is the complete article for Best Python NLP libraries, You check it out. NLTK is one of the good options for text processing but there are few more like Spacy, gensim, etc. This increases the space complexity as well as time complexity unnecessary. Because usually what people do is that they download the complete NLTK corpus. Well ! if you look the second line – nltk.download(‘averaged_perceptron_tagger’), Here we have to define exactly which package we really need to download from the NLTK package. Here you can see we have extracted the POS tagger for each token in the user string. Let’s see the complete code and its output here – Part of Speech Tagging using NLTK Let’s check out further – data_tokens_tag = pos_tag(data_token) ![]() If we refer the above lines of code then we have already obtained a data_token list by splitting the data string. ![]() This is a step we will convert the token list to POS tagging. Lets checkout the code – data ="Data Science Learner is an easy way to learn data science" Let’s take the string on which we want to perform POS tagging. Lets import – from nltk import pos_tag Step 3 – Here we will again start the real coding part. The above line will install and download the respective corpus etc. Nltk.download('averaged_perceptron_tagger') Here is the following code – pip install nltk # install using the pip package manager import nltk Part-Of-Speech tagging plays a vital role in Natural Language Processing. The Universal tagset of NLTK comprises only 12 coarse tag classes as follows: Verb, Noun, Pronouns, Adjectives, Adverbs, Adpositions, Conjunctions, Determiners, Cardinal Numbers, Particles, Other/ Foreign words, Punctuations. In this step, we install NLTK module in Python. Part of Speech Tagging using NLTK Python- Step 1 – This article will help you in part of speech tagging using NLTK python.NLTK provides a good interface for POS tagging. Part of speech is really useful in every aspect of Machine Learning, Text Analytics, and NLP. ![]()
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