Kuis Pemodelan Topik

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

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. Bagaimana LDA memperlakukan dokumen individual?
   • Sebagai milik tepat satu topik
   • Sebagai urutan di mana urutan kata sangat penting
   • Sebagai campuran beberapa topik dalam proporsi berbeda
   • Sebagai gambar teks yang akan dipindai
4. Apa asumsi 'sekantong kata' yang digunakan oleh LDA klasik?
   • Itu mengabaikan urutan kata dan hanya menggunakan jumlah kata
   • Itu hanya menyimpan kalimat pertama dari setiap dokumen
   • Ini menerjemahkan setiap dokumen ke dalam bahasa Inggris terlebih dahulu
   • Hal ini membutuhkan kata-kata yang disusun berdasarkan abjad
5. Keputusan manakah yang biasanya harus Anda ambil sebelum menjalankan LDA?
   • Judul persis setiap topik
   • Penulis setiap dokumen
   • Font yang digunakan untuk menulis dokumen
   • Jumlah topik K yang ditemukan
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