Module 8 Review

1. 1What is an option available in SAS EM for weightings?

   1. AInverse Document Frequency (IDF)
   2. BLog
   3. CBinary
   4. DCounts
2. 2What is the Interactive Filter Viewer?

   1. AIt allows you to search for documents in the corpus based on search expressions
   2. BIt allows you to web scrap
   3. CIt allows you to filter out specific words
   4. DIt allows you to change a dataset
3. 3Which type of text analysis takes syntax and semantics into account to understand and classify text?

   1. ALinguistic (NLP)
   2. BBag of Words
   3. CHybrid
   4. DLemmatization
4. 4Which of the following is not contained in a typical corpus?

   1. ALarge number of high frequency terms
   2. BA large number of infrequent terms
   3. CAn average number of average frequency terms
   4. DA small number of high frequency terms
5. 5 In Phrase and Entity Recognition, which option most appropriately describes Rule-Based Models?

   1. AThis model uses a combination of lexicons, word shapes, and grammar rules to identify named entities.
   2. BThis model is a supervised classification model that predicts the probability that
      a word corresponds to a particular class label.
   3. CThis model attempts to model the most likely sequence of labels given a sequence of terms
      by maximizing the joint probability of terms and labels.
   4. DThis model is designed to model the conditional probability of a label sequence given a
      sequence of words.
6. 6Which is NOT considered a clustering algorithm?

   1. ADecision Tree
   2. BExpectation-Maximization
   3. CNeural Networks
   4. DK-Means Clustering
7. 7How many topics can be contained in one document?

   1. AAll of the above
   2. BZero
   3. COne
   4. DMany
8. 8Which is not true in full singular value decomposition (SVD)?

   1. AThere is some loss of information
   2. BThere is no dimensionality reduction
   3. CCan be used in least squares approximation
   4. DIt decomposes a matrix into a product of three simpler matrices
9. 9What is Lemmatization?

   1. AIt is the process of applying normalization to tokens to reduce complexity.
   2. BIt is the counting of the words in a text to summarize and classify text documents.
   3. CIt is the process of converting a stream of characters and breaking it down into tokens.
   4. DIt is the process of considering syntax and semantics for understanding and classifying text.
10. 10___is a collection of terms that define a theme or an idea.

    1. ATopic
    2. BStem
    3. CGroup
    4. DPhrase

11. 11Which Model attempts to model the most likely sequence of labels with a sequence of terms by maximizing the joint probability of terms and labels?

    1. AHidden Markov Model (HMM
    2. BConditional Random Fields (CRF)
    3. CExpectation-Maximization
    4. DNone of the above
12. 12___ is used to extract more sophisticated features from text beyond token.

    1. AParts of Speech Tags
    2. BTokenization
    3. CStemming
    4. DLemmatization
13. 13In Zipf’s Law what does ‘r’ stand for?

    1. ARank of the word
    2. Bfrequency of a word
    3. CTotal number of stop words
    4. DTotal number of documents
14. 14__ is used to measure distance between clusters in hierarchical clustering algorithm.

    1. AEuclidean distance
    2. BHamming distance
    3. CManhattan distance
    4. DMinkowski distance

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Test sobre Module 8 Review, creado por Nick Feldman el 01/05/2022.

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