TF-IDF uye Bag-ye-Mazwi Mienzaniso Mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeupi ruzivo rwakakosha rwunoraswa nebhegi-remazwi?
   • Kurongeka uye girama yemazwi
   • Shoko rinokosha
   • Izwi remazwi
   • Kureba kwegwaro
2. Chii chinoita chikamu cheIDF cheTF-IDF?
   • Inosimudzira mazwi akajairika pamagwaro ese
   • Mazwi akaderera anooneka mumagwaro akawanda
   • Inoverenga mavara
   • Inoturikira mazwi asingawanzo
3. Izwi rinowanikwa mugwaro rega rega riri muunganidzwa rinowana kukosha kweIDF pedyo nei?
   • Chiuru chimwe
   • Negative infinity
   • Zero
   • Gwaro kuverenga
4. Ko TF-IDF mamakisi yetemu inoverengerwa sei?
   • TF kubvisa IDF
   • TF yakakamurwa neIDF
   • TF pamwe nekureba kwegwaro
   • TF yakapetwa neIDF
5. Ndeipi chiyero chekufanana chinowanzoshandiswa kuenzanisa TF-IDF gwaro mavheta?
   • Cosine kufanana
   • Rongedza chinhambwe
   • Kureva kukanganisa kwakapetwa kaviri
   • Bit count
6. Sei TF-IDF ichipa zvibodzwa zvepamusoro kune mamwe mazwi?
   • Iwo mazwi marefu
   • Anowanikwa mune rimwe gwaro asi haawanzo kuunganidzwa
   • Anoonekwa mugwaro rega rega
   • Vakaiswa mazita makuru
7. In a bag-of-words representation, what does a vector position usually correspond to?
   • A sentence’s original word position
   • A document’s publication date
   • A term in the vocabulary
   • A grammatical parse-tree branch
8. In the common IDF formula log(N/df), what does df represent?
   • The number of times a word appears in one document
   • The total number of words in the vocabulary
   • The number of labels assigned to a document
   • The number of documents containing the term