![]() or Elizabeth and Julie met at Karan house. Let’s say you want some particular patterns to match in corpus like → you want sentence should be in form “ PROPN met anyword? anyword? PROPN ”.(? → represents 0 or 1 time and PROPN → Proper Noun). Saving this dependency image: from pathlib import Path svg = (doc, style="dep",jupyter=False) output_path = Path("dependency_plot.svg") #path with file name output_path.open("w", encoding="utf-8").write(svg) Let’s dive into some advance topic → spaCy custom pos pattern matcher Instead of using sent_tokenize you can directly put whole text in nltk.pos_tag spaCy pos tagger import spacy nlp = spacy.load('en_core_web_lg') sentence = "He was being opposed by her without any reason.\ A plan is being prepared by charles for next project" for token in nlp(sentence): print(f'')Īs you can see in above image - He is tagged as PRON(proper noun) was as AUX(Auxiliary) opposed as VERB and so on… You should checkout universal tag list here. ![]() Let’s try nltk and spacy pos taggers NLTK pos tagger import nltk from nltk.tokenize import word_tokenize, sent_tokenize sentence = "He was being opposed by her without any reason.\ A plan is being prepared by charles for next project" for sent in sent_tokenize(sentence): wordtokens = word_tokenize(sent) print(nltk.pos_tag(wordtokens),end='\n\n') Checkout paper : The Surprising Cross-Lingual Effectiveness of BERT by Shijie Wu and Mark Dredze here. Accuracies on various English treebanks are also 97% (no matter the algorithm HMMs, CRFs, BERT perform similarly). One study found accuracies over 97% across 15 languages from the Universal Dependency (UD) treebank (Wu and Dredze, 2019). The accuracy of part-of-speech tagging algorithms is extremely high. ![]()
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