quiz bu lëkkale ak tegtal · Diggante Niveau

Topic Modeling Quiz

Xoolaat LDA topic distribution, dokimaa yu amul etiket, topic jaxase, baat-ordre gis-gis, topic lim, embeddings, tur nit, ak c-TF-IDF.

Yooni njiit yu jëm ciModeling topic
Laaj 1 de 8

In the LDA formulation, how is a modeled topic represented over the vocabulary?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  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. naka la LDA di doxalee ci benn këyit?
    • Dafa bokk ci benn topic
    • Dafay am solo lool ñu toppalante baat yi
    • Ni njaxasu topic yu bari ci anam yu wuute
    • Ni nataalu mbind buñu wara skane
  4. Lan mooy 'sac-de-words' bi LDA classic di jëfandikoo?
    • Du bàyyi xel ci ni baat yi di doxee, limu baat yi kese lay jëfandikoo
    • Dafay tëye xët wi njëkk ci këyit bu nekk
    • Dafay njëkka tekki bépp këyit ci Àngle
    • Dafay laaj baat yi ñu toppalante ci alfabet bi
  5. Ban dogal ngay wara jël balaa ngay tàmbali LDA?
    • Titre bu dëggu bu topic bu nekk
    • Auteur bu bépp këyit
    • Font biñu bindee këyit yi
    • Limu topic K yi nga wara wut
  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