We then initialize an instance of nltk.RegexpParser() with this grammar and use it to parse the tokenized sample sentence. Then we define the grammar for Noun Phrase as NP: which means that a chunk will be constructed when an optional Determiner (DT) is followed by any number of Adjective (JJ) or Noun (NN). Going by the steps we explained above, in the below example, we first tokenize the sample sentence and perform POS Tagging on it. The above step produces the result which can either be printed as it is or we can draw a graph for better visualization. Using this grammar, we create a chunk parser with the help of RegexpParser and apply it to our sentence. This is a very important step because grammar lays the rule of chunking. Step 2:ĭefine the grammar to perform chunking. Tokenize the sentence and perform POS Tagging. The process of chunking in NLTK is a multi-step process as explained below – Step1 : It should be noted that POS tagging is the prerequisite for the chunking process and the chunks do not overlap with each other.Ĭhunking is essential for understanding the semantics of the text and helps in information retrieval. In NLP, chunking is the process of breaking down a text into phrases such as Noun Phrases, Verb Phrases, Adjective Phrases, Adverb phrases, and Preposition Phrases.Ĭhunking is commonly used to extract Noun Phrases (NP) from the sentence. Chunking can help us to take us to the next level. But just doing this does not give us enough meaningful information about the sentence. We have seen that we can break down a sentence into tokens of words and then do POS tagging for identifying parts of speech for those words. [how, however, whence, whenever, where, whereby… [that, what, whatever, whatsoever, which, who,… [bases, reconstructs, marks, mixes, displeases… [predominate, wrap, resort, sue, twist, spill,… Verb, present tense, not 3rd person singular [multihulled, dilapidated, aerosolized, chaire… [telegraphing, stirring, focusing, angering, j… [dipped, pleaded, swiped, regummed, soaked, ti… [ask, assemble, assess, assign, assume, atone,… [Goodbye, Goody, Gosh, Wow, Jeepers, Jee-sus, … [aboard, about, across, along, apart, around, … [best, biggest, bluntest, earliest, farthest, … [further, gloomier, grander, graver, greater, … [occasionally, unabatingly, maddeningly, adven… [hers, herself, him, himself, hisself, it, its… [undergraduates, scotches, bric-a-brac, produc… [Americans, Americas, Amharas, Amityvilles, Am… [Motown, Venneboerger, Czestochwa, Ranzer, Con… [common-carrier, cabbage, knuckle-duster, Casi… [can, cannot, could, couldn’t, dare, may, migh… [calmest, cheapest, choicest, classiest, clean… [bleaker, braver, breezier, briefer, brighter,… [third, ill-mannered, pre-war, regrettable, oi… [astride, among, uppon, whether, out, inside, … Preposition or conjunction, subordinating [gemeinschaft, hund, ich, jeux, habeas, Haemen… [all, an, another, any, both, del, each, eithe… [mid-1890, nine-thirty, forty-two, one-tenth, … [&, ‘n, and, both, but, either, et, for, less,…
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