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Latte toa), , Assign predefined categories to a document, , a, , , , , , , , , , , , , , , , , The translation of text by a computer, with no human, involvement. 4, , , , humans with machines., , , , Summarising the contextual meaning of documents and, information and also understanding the emotional meanings, hidden inside the information Qa, , Find sentiment among the numerous posts or even in the, same post where emotion is not always clearly expressed., |, , FE Creating a constant and interactive interface between, , , , eS, , , , , , , , , , , , Al project cycle consists of five main processes. Identify A, B, C, D, E in the cycle given below., , Problem Pooping, Date Acquistion, Rata Exp lrotton, Ntode) ling, , , , ARTIFICIAL INTELLIGENCE | Kips \ 293, , , , Elbot Steve Worswick, , Mitsuku, 3. | Rose, 4, | Jabberwacky, , , , , , , , , | Rollo Carpenter, , c. | FredRoberts i, Sd. uce Wilcox
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low are su programming languages, can you write their full forms?, , . BASIC, 3. COBOL Common Bustnais—Orianted language, 4. AMOS Arm'sn Oberating System, , , , , , i ale, 5. SAIL —Slvnford Artificial intelligence Jantuage, , 2. Give some examples of cases of different syntax and same semantics and vice-versa., , Area Ried, , laa es 4+ 4, , , , , , , , 3. Mention some words which can have multiple meanings and explain the meanings by using them in, sentences, , Post !- Subsceuent to oraf tue, Job or paid zmp loyment, Bots - An Cguibment for s ports , A_Mammel, , , , Fair i= Light »Fine & oly, An everd for Public, , 4. Given some examples of sentences having correct syntax and incorrect semantics., , G or Shoei, , . Pink ball’ cow fn PoUaU SIZ08., , (up) “The circle was a right Btongle., , , , , , , for the whole textual data from all the documents altogether is called orb, L $ :, cs very popular model that explains the occurrence of each word within a document., , Statistical measure that evaluates the importance of a word to a document in a corpus., Ables us to assign predefined categories to a document., , coring value tnoreaté in proportion to the number of times a word appears in the, iment., , Term prey uncy and Inverse Document frequency
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SST: ; vara oe, , UIZ 2: Multiple- cheice Questions in te i ; . ., ‘ou have been given a set of five MCQs based on the chapter. Each aulection fee four options. Choose the, , option that suits you the best. You are advised to go to the ‘relevant portion of the chapter to dig for more, information about the questions in hand., , , , , , , , , , , ak Natural Language Processing (NLP) lies in 4. Simplification of human language in order, ; which of the following fields? to be easily understood by computers is, a (a) Artificial Intelligence called?, (b) Computer Science (a) Sentence Planning, ‘ (c) Only (b) f _ 4) Text Normalisation, _JoBoth (a) and (b) (c) Text Realisation, 4 : " (d) None of the above, 2 NLP can be useful in which of the following, areas? 5. Which of the following is used for finding, (a) Automatic Text Summarisation the frequency of words in some given text, ?, (b) Virtual Assistants sample?, (a) Stemming, , (c) Machine Translation “i ea, Af All of the mentioned __ (b) Lemmatisation, se} Bag of words, , 3. Machine Translation feature converts (d) None of the above, , , , _Jayone human language to another, (b) human language to machine language, , (c) any human language to English, (d) Machine language to human language
