{"text": "### Human: Membangun fungsi untuk mengklasifikasikan kata-kata menjadi kata benda, kata kerja, dan kata sifat.. ### Assistant: Salah satu cara untuk mengklasifikasikan kata-kata ke dalam bagian mereka dari pidato adalah dengan menggunakan teknik pemrosesan bahasa alami (NLP) seperti tagging Parts of Speech (POS). Di sini, saya akan membangun fungsi menggunakan Python's Natural Language Toolkit (NLTK) library yang mengelompokkan kata-kata menjadi kata benda, kata kerja, dan kata sifat: `` ` python import nltk dari nltk.corpus import wordnet nltk.download'(averaged_percep_taggertron') nltk.download'wordnet') def classi_words: # gunakan nltk library untuk memeriksa POS part_of_speech = nltk.post_advtag [word]) [0] [1] #definisi tag yang mewakili no verbs, adjectives, dan adjectives_advtags (\"NN\", \"NS\", \"NNP\", \"NNN\") jika tidak dapat mengembalikan bagian dari kata kata kerja yang diharapkan seperti tag \"B\" atau \"NN\" (JB\", \"N\" = \"B\"), \"N\" (N\" = \"N\" = \"N\"), \"N\" (N\" = \"N\" = \"N\" = \"N\"), \"N\" (N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" = \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N\" \"N"}