<prompt> Theha mosebetsi ho arola mantsoe ka mabitso, maetsi le mahlaodi.nan  <response>: Instruction in English: Construct a function to classify words into nouns, verbs, and adjectives. . Response in English: One way to classify words into their part of speech is to use natural language processing NLP techniques such as Parts of Speech POS tagging. Here, I will construct a function using Pythons Natural Language Toolkit NLTK library that classifies words into nouns, verbs, and adjectives:pythonimport nltkfrom nltk.corpus import wordnetnltk.downloadaveraged_perceptron_taggernltk.downloadwordnetdef classify_wordsword: use the nltk library for POS tagging part_of_speech nltk.pos_tagword01 define the POS tags that represent nouns, verbs, and adjectives noun_tags NN, NNS, NNP, NNPS verb_tags VB, VBD, VBG, VBN, VBP, VBZ adjective_tags JJ, JJR, JJS check if the POS tag of the given word matches any of the defined tags if part_of_speech in noun_tags: return Noun elif part_of_speech in verb_tags: return Verb elif part_of_speech in adjective_tags: return Adjective else: return OtherTo test this function, lets try classifying different words:python Testing the classify_words functionprintclassify_wordscat expected result: Nounprintclassify_wordsjump expected result: Verbprintclassify_wordshappy expected result: Adjectiveprintclassify_wordsquickly expected result: Adverb Note that this example solution can correctly identify nouns, verbs, and adjectives, but would not be able to classify other parts of speech such as adverbs correctly as the function is designed to only return one of Noun, Verb, Adjective, or Other. . Response in Sesotho: Tsela e ngoe ea ho arola mantsoe karolong ea puo ea bona ke ho sebelisa mekhoa ea tlhaho ea puo ea tlhaho NLP joalo ka likarolo tsa puo POS. Mona, ke tla etsa mosebetsi ke sebelisa laeborari ea Pythons Natural Language Toolkit NLTK e hlophisang mantsoe ka mabitso, maetsi, le mahlaodi: python import nltk from nltk.corpus import wordnet nltk.downloadaveraged_perceptron_tagger nltk.download. wordnet def classify_wordslentswe: sebedisa laeborari ya nltk bakeng sa ho kenya POS tagging part_of_speech nltk.pos_taglentsoe01 hlalosa li-tag tsa POS tse emelang mabitso, maetsi, le mahlaodi noun_tags NN, NNS, NNP, NNPS verb_tags VB, VBD, VBG, VBN, VBP, VBZ adjective_tags JJ , JJR, JJS hlahloba hore na letšoao la POS la lentsoe le fanoeng le lumellana le leha e le efe ea li-tag tse hlalositsoeng haeba karolo_ea_speech ho li-noun_tags: khutlisetsa Noun elif part_of_speech in verb_tags: return Verb elif part_of_speech in adjective_tags: return Noun Lehlaodi le leng: kgutlisa Tse ding Ho leka tshebetso ena, ha re lekeng ho hlopha mantswe a fapaneng: python Ho lekola mongolo wa classify_words printclassify_wordscat sephetho se lebeletsweng: Noun printclassify_words tlola sephetho se lebelletsoeng: Khatiso ea leetsiclassify_wordshappy sephetho se lebeletsoeng: Khatiso ea lehlalosiclassify_wordska potlako sephetho se lebelletsoeng: Leetsi Hlokomela hore tharollo ea mohlala ona e ka khetholla mabitso ka nepo, maetsi, le mahlaodi, empa e ne e ke ke ya kgona ho hlopha dikarolo tse ding tsa puo jwalo ka mahlalosi ka nepo kaha tshebetso e etseditswe ho kgutlisa le le leng feela la Lebitso, Leetsi, lekgethi, kapa Tse ding. 