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Python - PoS Tagging and Lemmatization using spaCy
spaCy is one of the best text analysis library. spaCy excels at large-scale information extraction tasks and is one of the fastest in the world. It is also the best way to prepare text for deep learning. spaCy is much faster and accurate than NLTKTagger and TextBlob.
How to Install?
pip install spacy python -m spacy download en_core_web_sm
#importing loading the library import spacy # python -m spacy download en_core_web_sm nlp = spacy.load("en_core_web_sm") #POS-TAGGING # Process whole documents text = ("""My name is Vishesh. I love to work on data science problems. Please check out my github profile!""") doc = nlp(text) # Token and Tag for token in doc: print(token, token.pos_) # You want list of Verb tokens print("Verbs:", [token.text for token in doc if token.pos_ == "VERB"]) #Lemmatization : It is a process of grouping together the inflected #forms of a word so they can be analyzed as a single item, #identified by the word’s lemma, or dictionary form. import spacy # Load English tokenizer, tagger, # parser, NER and word vectors nlp = spacy.load("en_core_web_sm") # Process whole documents text = ("""My name is Vishesh. I love to work on data science problems. Please check out my github profile!""") doc = nlp(text) for token in doc: print(token, token.lemma_)
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