주제 모델링 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

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. LDA는 개별 문서를 어떻게 처리합니까?
   • 정확히 하나의 주제에 속하므로
   • 어순이 필수적인 순서로
   • 여러 주제를 다양한 비율로 혼합하여
   • 스캔할 텍스트의 이미지로
4. 고전적인 LDA에서 사용되는 '단어주머니' 가정은 무엇입니까?
   • 단어 순서를 무시하고 단어 수만 사용합니다.
   • 각 문서의 첫 번째 문장만 유지합니다.
   • 모든 문서를 먼저 영어로 번역합니다
   • 단어가 알파벳 순서로 되어 있어야 합니다.
5. LDA를 실행하기 전에 일반적으로 어떤 결정을 내려야 합니까?
   • 각 주제의 정확한 제목
   • 모든 문서의 작성자
   • 문서가 작성된 글꼴
   • 찾아야 할 주제 K의 수
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