<prompt> Hlalosa mantsoe a tokenization, stemming le lemmatization tsebetsong ea puo ea tlhaho.nan  <response>: Instruction in English: Describe the terms tokenization, stemming and lemmatization in natural language processing. . Response in English: Tokenization, stemming, and lemmatization are all essential preprocessing techniques used in natural language processing NLP to clean and prepare textual data for modeling or analysis.Tokenization is the process of breaking down a text into individual words or tokens. It is the initial step in text preprocessing and is essential in enabling algorithms to analyze and derive meaning from text data. In tokenization, algorithms segment a sentence into its constituents, separating punctuation and whitespace, to produce a list of tokens or words. These tokens serve as input features for various NLP tasks such as information retrieval, text classification, and sentiment analysis, amongst others.Stemming is the process of reducing words to their root or base form. It is a rule-based approach to remove inflectional suffixes and affixes from words in order to obtain a root form that may not necessarily be a real word in the language. For example, the stemming algorithm might reduce the words running, runner, and ran to the stem run. The goal of stemming is to map related words to a common base form, which can aid in tasks such as information retrieval and text classification.Lemmatization is similar to stemming in that it also reduces words to their base forms. However, unlike stemming, lemmatization is a more sophisticated technique that employs a dictionary or morphological analysis to derive the root form of a word. The output of lemmatization is always a real word in the language, making it a more linguistically accurate technique than stemming. For instance, while the stemming algorithm might reduce the word better to bet, a lemmatization algorithm would correctly return good as the root form.Both stemming and lemmatization serve to reduce dimensionality and data sparsity, making it easier for algorithms to interpret and analyze text data. . Response in Sesotho: Tokenization, stemming, le lemmatization kaofela ke mekhoa ea bohlokoa ea ho lokisa e sebelisoang tsebetsong ea puo ea tlhaho NLP ho hloekisa le ho lokisa lintlha tsa mongolo bakeng sa mohlala kapa tlhahlobo. Tokenization ke mokhoa oa ho arola mongolo ka mantsoe kapa li-tokens. Ke mohato oa pele oa ho lokisa mongolo me o bohlokoa ho nolofalletsang li-algorithms ho sekaseka le ho fumana moelelo ho tsoa ho data ea mongolo. Ka tokenization, algorithms e arola polelo ho likarolo tsa eona, ho arola matšoao le sebaka se sesoeu, ho hlahisa lethathamo la matšoao kapa mantsoe. Li-tokens tsena li sebetsa e le likarolo tsa ho kenya mesebetsing e fapaneng ea NLP joalo ka ho khutlisa tlhahisoleseling, ho hlophisa mongolo, le tlhahlobo ea maikutlo, hara tse ling. Stemming ke mokhoa oa ho fokotsa mantsoe motso kapa sebopeho sa motheo. Ke mokhoa o theiloeng melaong oa ho tlosa li-suffixes le li-affixes mantsoeng e le ho fumana sebopeho sa motso seo e kanna ea se be lentsoe la sebele puong. Mohlala, algorithm ea stemming e ka fokotsa mantsoe matha, semathi, le mathela ho stem run. Sepheo sa ho hlahisa maikutlo ke ho etsa mapa oa mantsoe a amanang le mokhoa o tloaelehileng oa motheo, o ka thusang mesebetsing e kang ho fumana tlhahisoleseding le ho hlophisa mongolo. Lematization e tsoana le stemming ka hore e boetse e fokotsa mantsoe mefuteng ea bona ea motheo. Leha ho le joalo, ho fapana le stemming, lemmatization ke mokhoa o tsoetseng pele haholoanyane o sebelisang bukana e hlalosang mantsoe kapa tlhahlobo ea morphological ho fumana mofuta oa motso oa lentsoe. Sephetho sa lemmatization kamehla ke lentsoe la sebele puong, e leng se etsang hore e be mokhoa o nepahetseng oa lipuo ho feta ho thibela. Mohlala, leha algorithm ea stemming e ka fokotsa lentsoe betere ho becha, algorithm ea lemmatization e ka khutlisa ntle joalo ka motso. Ka bobeli stemming le lemmatization li thusa ho fokotsa boholo le ho fokotseha ha data, ho nolofaletsa li-algorithms ho fetolela le ho sekaseka data ea mongolo. 