Boolean Retrieval Model

Question 1

What is the boolean retrieval model?

Answer 1

A method of information retrieval that can answer any query that is a boolean expression

Question 2

What is a boolean query?

Answer 2

Queries using AND, OR, and NOT to join query terms

Question 3

What is a limitation of the boolean retrieval model?

Answer 3

Only records if a document matches a condition or not, no additional data such as frequency or proximity.

Question 4

What is a phrase query?

Answer 4

Strings of multiple tokens that are meant to be used together.
Ex: “University of Toronto”

Question 5

Why can’t the classic boolean model perform phrase queries?

Answer 5

It doesn’t record information about proximity of terms to one another

Question 6

What is a biword index?

Answer 6

Indexing every consecutive pair of terms

Question 7

What are the benefits of biword indexes?

Answer 7

1. Two word phrase query processing is immediate
2. Can search for longer phrases with some proximity info

Question 8

How can longer phrase queries be processed with a biword index?

Answer 8

Breaking down the query into a list of biwords. Perform separate query on each biword and conjunct results

Question 9

What are the downsides of a biword index?

Answer 9

1. False positives as we cannot verify the whole contiguous string appears
2. Index takes more storage due to the bigger dictionary

Question 10

What is a positional index?

Answer 10

In the postings table, store document frequency as well as positions in which the token appears

Question 11

How do you process a phrase query using a positional index?

Answer 11

1. Extract index entries for each term
2. Merge their doc:position lists to enumerate all positions between words

Question 12

Why can’t biword indexes be used for longer phrase queries?

Answer 12

The issue of false positives. It only checks if pairs of words are present beside each other not the contiguous series

Question 13

What is the drawback of a positional index and why is it used regardless?

Answer 13

It expands postings storage substantially as we store every occurrence of each term. We use it anyway because it brings value through phrase and proximity queries

Question 14

What are the rules of thumb about positional index size?

Answer 14

1. A positional index is 2-4 times as large as a non-positional index
2. A positional index is 35-50% of the volume of the original text

Question 15

How can we combine the biword and positional index?

Answer 15

Store the positions of each biword combination

Question 16

What is the size of a biword positional index?

Answer 16

About 26% bigger than a positional index alone