11 Questions
What is the main purpose of factor analysis of the document-term matrix?
To determine the similarity of words based on the documents they cooccur in and the similarity of documents based on the words they contain.
What does Singular Value Decomposition (SVD) help in achieving in the context of document-term matrix?
It helps in obtaining the best (least-squares) approximation by truncating the matrix to topics.
How can the complexity of SVD on a matrix be reduced?
By approximating it using Monte-Carlo sampling if only a certain number of components are needed.
What is the key idea behind the probabilistic topic modeling?
Every document and every word is considered as a mixture of topics.
What does a probabilistic topic model entail for every document and word?
For every document: a topic distribution, and for every word: a word distribution for every topic.
Does SVD yield probabilities directly? Explain.
No, SVD does not yield probabilities directly, but the factors obtained can contain negative values.
What is the main goal of topic modeling in relation to a text corpus?
Find the latent structure that resembles topics and best summarizes the collection.
How does Latent Semantic Indexing (LSI) or Latent Semantic Analysis (LSA) help in information retrieval?
It helps address challenges like synonymy and polysemy in information retrieval.
What distinguishes topic modeling from clustering in terms of emphasis?
In clustering, the emphasis is on data points/documents, while in topic modeling, the emphasis is on the topics/clusters themselves.
Why is exact search problematic in information retrieval when dealing with synonymy and polysemy?
Exact search will not find synonyms and will include polynyms and homonyms.
What is the purpose of identifying 'factors' in Latent Semantic Indexing for document representation?
Factors provide a lower-dimensional representation of the document.
This quiz covers the concept of topic modeling, focusing on finding the latent structure in a text corpus to identify topics or concepts that best summarize the collection. It also includes an overview of Latent Semantic Analysis (LSA) for extracting topics via matrix factorization.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
