CS267 Spring 2016Practice Midterm

To study for the midterm I would suggest you: (1) Know how to do (by heart) all the practice problems. (2) Go over your notes at least three times. Second and third time try to see how much you can remember from the first time. (3) Go over the homework problems. (4) Try to create your own problems similar to the ones I have given and solve them. (5) Skim the relevant sections from the book. (6) If you want to study in groups, at this point you are ready to quiz each other. The practice midterm is below. Here are some facts about the actual midterm: (a) It is closed book, closed notes. Nothing will be permitted on your desk except your pen (pencil) and test. (b) You should bring photo ID. (c) There will be more than one version of the test. Each version will be of comparable difficulty. (d) One problem (less typos) on the actual test will be from the practice test.

1. Define the following information retrieval concepts: (a) probability ranking principle, (b) specificity, (c) language model, (d) maximum likelihood estimate.
2. What does it mean to smooth a first order language model? Give an example where (a) smoothing would assign non-zero probability to a phrase that might previously have had 0 probability, (b) smoothing would reduce the probability of phrase from that of the non-smoothed model.
3. Give the pseudo-code for the nextPhrase(t[1],t[2], .., t[n], position) algorithm from class. Give its run time under different implementations of the next(t,position) function (no need for proofs).
4. Give the PHP code to read in a file my_integer.txt which stores an integer as a string. Your program should then output to the default output stream this value, increment the value, and write it back to disk.
5. Briefly describe the galloping search implementation of the next(t, position) function.
6. Give pseudo-code for the cosineRank algorithm from class together with a short explanation in English of what it is doing.
7. Define or give the equation of the following concepts: (a) a cover of a term vector, (b) the proximity scoring function, (c) docRight(Q, u), (d) precision@k.
8. Briefly explain how autoloading works in PHP. What is Composer? Give an example of a couple of its commands.
9. Give an example of a couple of the transformations that the Porter Stemmer performs. Explain char-gramming. What is dictionary interleaving? What is a per-term index?
10. In English explain how sort-based and merge-based inverted index construction are done. Give the formula for BM25 and one example of its use.