<prompt> Etsa tlhaloso ea taba ea tšebeliso ea Named Entity Recognition NER.nan  <response>: Instruction in English: Create a description of the use case of Named Entity Recognition NER. . Response in English: Named Entity Recognition NER is a sub-task of Natural Language Processing NLP that involves identifying and categorizing named entities within a text into pre-defined categories such as person names, organization names, locations, dates, and other relevant information. The main use case of NER is to extract structured information from unstructured text data, which can be used in several ways. For instance, information extracted using NER can be used to improve the accuracy of search engine results by recognizing and disambiguating between different entities with the same name. Another use case is to identify relationships between entities to build knowledge graphs for semantic analysis. NER can also be used to enhance data mining by pre-processing text data to identify named entities and categorize them for easy retrieval. Additionally, NER can be used to automate and improve the efficiency of several business processes, such as document classification, customer support, content personalization, and recommendation systems. In summary, Named Entity Recognition is a powerful tool in the field of Natural Language Processing to extract and organize valuable information from large amounts of text data for various applications. . Response in Sesotho: Recognition Entity Entity NER ke mosebetsi o monyane oa Natural Language Processing NLP o kenyelletsang ho tsebahatsa le ho arola mekhatlo e nang le mabitso ka hara sengoloa ka mekhahlelo e boletsoeng esale pele joalo ka mabitso a batho, mabitso a mekhatlo, libaka, matsatsi, le lintlha tse ling tse amehang. Taba ea mantlha ea tsebeliso ea NER ke ho ntša tlhaiso-leseling e hlophisitsoeng ho tsoa ho data ea mongolo e sa hlophisoang, e ka sebelisoang ka mekhoa e mengata. Mohlala, tlhaiso-leseling e ntšitsoeng ho sebelisoa NER e ka sebelisoa ho ntlafatsa bonnete ba sephetho sa enjine ea patlo ka ho lemoha le ho se utloisise lipakeng tsa mekhatlo e fapaneng e nang le mabitso a tšoanang. Taba e ngoe ea tsebeliso ke ho tseba likamano lipakeng tsa mekhatlo ho aha li-graph tsa tsebo bakeng sa tlhahlobo ea semantic. NER e ka boela ea sebelisoa ho ntlafatsa merafo ea data ka ho lokisa data ea mongolo esale pele ho hloaea mekhatlo e nang le mabitso le ho e arola hore e fumanehe habonolo. Ho feta moo, NER e ka sebelisoa ho iketsetsa le ho ntlafatsa tsebetso ea litsebetso tse maloa tsa khoebo, joalo ka ho hlopha litokomane, tšehetso ea bareki, ho iketsetsa motho ka mong, le litsamaiso tsa likhothaletso. Ka kakaretso, Named Entity Recognition ke sesebelisoa se matla tšimong ea Natural Language Processing ho ntša le ho hlophisa boitsebiso ba bohlokoa ho tsoa ho data e ngata ea mongolo bakeng sa likopo tse fapaneng. 