Topic Modelling Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. In the LDA formulation, how is a modeled topic represented over the vocabulary?
   • As one mandatory human-written keyword only
   • As a probability distribution over words
   • As a grammatical parse of every sentence
   • As a sentiment value with no vocabulary representation
2. Why can an unsupervised LDA topic model be fit to documents without topic labels?
   • It requires a verified category for every document first
   • It reads only documents with identical titles
   • It necessarily uses a classifier trained on the same task labels
   • It infers unobserved topic structure from the observed words
3. LDA inobata sei gwaro rega?
   • Sezvo iri yenyaya imwe chete
   • Sekutevedzana uko kurongeka kwemazwi kwakakosha
   • Semusanganiswa wemisoro yakawanda muzvikamu zvakasiyana
   • Semufananidzo wechinyorwa chinozoongororwa
4. Chii chinonzi 'bag-of-words' fungidziro inoshandiswa neLDA yekare?
   • Inofuratira kurongeka kwemazwi uye inoshandisa chete kuverenga mazwi
   • Inongochengeta mutsara wekutanga wegwaro rega rega
   • Inoturikira gwaro rega rega muchirungu kutanga
   • Zvinoda kuti mazwi aite munhevedzano yearufabheti
5. Ndeipi sarudzo yaunofanirwa kuita usati watanga LDA?
   • Musoro chaiwo wemusoro wega wega
   • Munyori wegwaro rega rega
   • Mafonti akanyorwa magwaro
   • Huwandu hwemisoro K yekuwana
6. Which stage commonly starts BERTopic's grouping of documents rather than relying only on raw word counts?
   • Assigning every document a manually required topic label
   • Clustering representations derived from document embeddings
   • Removing all text before computing any representation
   • Treating every document as an identical image file
7. When inspecting LDA's high-probability words, who can supply a descriptive topic name such as election coverage?
   • An analyst interpreting the weighted words and documents
   • A probability value that necessarily proves one official name
   • A required government approver for every model run
   • A document filename that always determines the meaning
8. After BERTopic groups documents, what does c-TF-IDF help produce for each cluster?
   • The original input image pixels
   • A required dependency parse for every token
   • Characteristic weighted words representing the group
   • Proof that every cluster has exactly one true theme