Introduction to Information Retrieval

CS276

Information Retrieval and Web Search

Pandu Nayak and Prabhakar Raghavan

Lecture 1: Boolean retrieval

Information Retrieval

• Information Retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers)

Unstructured data in 1680

• Which plays of Shakespeare contain the words Brutus AND Caesar but NOT Calpurnia?
• One could grep all of Shakespeare’s plays for Brutus and Caesar, then strip out lines containing Calpurnia?
• Why is that not the answer?
• Slow (for large corpora)
• NOT Calpurnia is non-trivial
• Other operations (e.g., find the word Romans near countrymen) not feasible
• Ranked retrieval (best documents to return)
• Later lectures

Term-document incidence

Brutus AND Caesar BUT NOT                     1 if play contains word

Calpurnia                                                                 0 otherwise

Incidence vectors

• So we have a 0/1 vector for each term.

• To answer query: take the vectors for Brutus, Caesar and Calpurnia (complemented)  → bitwise AND.

• 110100 AND 110111 AND 101111 = 100100.

Incidence vectors

• So we have a 0/1 vector for each term.

• To answer query: take the vectors for Brutus, Caesar and Calpurnia (complemented)  → bitwise AND.

• 110100 AND 110111 AND 101111 = 100100.

Basic assumptions of Information Retrieval

• Collection: Fixed set of documents
• Goal: Retrieve documents with information that is relevant to the user’s information need and helps the user complete a task.

How good are the retrieved docs?

• Precision : Fraction of retrieved docs that are relevant to user’s information need

• Recall : Fraction of relevant docs in collection that are retrieved

• More precise definitions and measurements to follow in later lectures

Bigger collections

• Consider N = 1 million documents, each with about 1000 words.

• Avg 6 bytes/word including spaces/punctuation

• 6GB of data in the documents.

• Say there are M = 500K distinct terms among these.

Can’t build the matrix

• 500K x 1M matrix has half-a-trillion 0’s and 1’s.

• But it has no more than one billion 1’s.                               Why?

• matrix is extremely sparse.

• What’s a better representation?

• We only record the 1 positions.

Inverted index

• For each term t, we must store a list of all documents that contain t.
• Identify each by a docID, a document serial number
• Can we use fixed-size arrays for this?
 Brutus → 1 2 4 11 31 45 173 174 Caesar → 1 2 4 5 6 16 57 32 Calpurnia → 2 31 54 101

What happens if the word Caesar is added to document 14?

Inverted index

• We need variable-size postings lists

• On disk, a continuous run of postings is normal and best

• In memory, can use linked lists or variable length arrays

• Some tradeoffs in size/ease of insertion                         Posting(below)

 Brutus → 1 2 4 11 31 45 173 174 Caesar → 1 2 4 5 6 16 57 32 Calpurnia → 2 31 54 101

Brutus , Caeser and Calpurnia are dictionaries.

Numbers are postings.

Sorted by docID (more later on why).

The index we just built

• How do we process a query?

• Later - what kinds of queries can we process?                ← Today’s focus

Query processing: AND

• Consider processing the query: Brutus AND Caesar

• Locate Brutus in the Dictionary;

• Retrieve its postings.

• Locate Caesar in the Dictionary;

• Retrieve its postings.

• Merge” the two postings:

The merge

• Walk through the two postings simultaneously, in time linear in the total number of postings entries

Query optimization

• What is the best order for query processing?

• Consider a query that is an AND of n terms.

• For each of the n terms, get its postings, then AND them together.

 Brutus → 2 4 8 16 32 64 128 Caesar → 1 2 3 5 8 16 21 34 Calpurnia → 13 16
Query: Brutus AND Calpurnia AND Caesar

Query optimization example

• Process in order of increasing freq:

↑

(This is why we kept document freq. in dictionary)

 Brutus → 2 4 8 16 32 64 128 Caesar → 1 2 3 5 8 16 21 34 Calpurnia → 13 16

Execute the query as (Calpurnia AND Brutus) AND Caesar.

Boolean queries: Exact match

• The Boolean retrieval model is being able to ask a query that is a Boolean expression:
• Boolean Queries use AND, OR and NOT to join query terms

• Views each document as a set of words

• Is precise: document matches condition or not.

• Perhaps the simplest model to build an IR system on

• Primary commercial retrieval tool for 3 decades.

• Many search systems you still use are Boolean:

• Email, library catalog, Mac OS X Spotlight

Boolean queries: More General Merges

• Exercise: Adapt the merge for the queries:

• Brutus AND NOT Caesar

• Brutus OR NOT Caesar

Can we still run through the merge in time O(x+y)?

What can we achieve?

Boolean queries: More General Merges

• Exercise: Adapt the merge for the queries:

• Brutus AND NOT Caesar

• Brutus OR NOT Caesar

Can we still run through the merge in time O(x+y)?

What can we achieve?

Example: WestLaw http://www.westlaw.com/

• Largest commercial (paying subscribers) legal search service (started 1975; ranking added 1992)

• Tens of terabytes of data; 700,000 users

• Majority of users still use boolean queries

• Example query:

• What is the statute of limitations in cases involving the federal tort claims act?

• LIMIT! /3 STATUTE ACTION /S FEDERAL /2 TORT /3 CLAIM

• /3 = within 3 words, /S = in same sentence

Example: WestLaw http://www.westlaw.com/

• Another example query:

• Requirements for disabled people to be able to access a workplace

• disabl! /p access! /s work-site work-place (employment /3 place)

• Note that SPACE is disjunction, not conjunction!

• Long, precise queries; proximity operators; incrementally developed; not like web search

• Many professional searchers still like Boolean search

• You know exactly what you are getting

• But that doesn’t mean it actually works better….

More general optimization

• e.g., (madding OR crowd) AND (ignoble OR strife)

• Get doc. freq.’s for all terms.

• Estimate the size of each OR by the sum of its doc. freq.’s (conservative).

• Process in increasing order of OR sizes.

Exercise

• Recommend a query processing order for
• (tangerine OR trees) AND

• (kaleidoscope OR eyes)

Term                            Freq

eyes                              213312

kaleidoscope                 87009

skies                             271658

tangerine                       46653

trees                             316812

Query processing exercises

• Exercise: If the query is friends AND romans AND (NOT countrymen), how could we use the freq of countrymen?

• Exercise: Extend the merge to an arbitrary Boolean query. Can we always guarantee execution in time linear in the total postings size?

• Hint: Begin with the case of a Boolean formula query where each term appears only once in the query.

Exercise

• Try the search feature at

• Write down five search features you think it could do better

• Beyond term search

• Stanford University

• Proximity: Find Gates NEAR Microsoft.

• Need index to capture position information in docs.

• Zones in documents: Find documents with
(author = Ullman) AND (text contains automata).

Evidence accumulation

• 1 vs. 0 occurrence of a search term

• 2 vs. 1 occurrence

• 3 vs. 2 occurrences, etc.

• Usually more seems better

• Need term frequency information in docs

Ranking search results

• Boolean queries give inclusion or exclusion of docs.

• Often we want to rank/group results

• Need to measure proximity from query to each doc.

• Need to decide whether docs presented to user are singletons, or a group of docs covering various aspects of the query.

IR vs. databases: Structured vs unstructured data

• Structured data tends to refer to information in “tables”

 Employee Manager Salary Smith Jones 50000 Chang Smith 60000 Ivy Smith 50000
• Typically allows numerical range and exact match

• (for text) queries, e.g.,

• Salary < 60000 AND Manager = Smith.

Unstructured data

• Typically refers to free-form text

• Allows

• Keyword queries including operators

• More sophisticated “concept” queries, e.g.,

• find all web pages dealing with drug abuse

• Classic model for searching text documents

Semi-structured data

• In fact almost no data is “unstructured”

• E.g., this slide has distinctly identified zones such as the Title and Bullets

• Facilitates “semi-structured” search such as

• Title contains data AND Bullets contain search

… to say nothing of linguistic structure

More sophisticated semi-structured search

• Title is about Object Oriented Programming AND Author something like stro*rup

• where * is the wild-card operator

• Issues:

• how do you process “about”?

• how do you rank results?

• The focus of XML search (IIR chapter 10)

Clustering, classification and ranking

• Clustering: Given a set of docs, group them into clusters based on their contents.

• Classification: Given a set of topics, plus a new doc D, decide which topic(s) D belongs to.

• Ranking: Can we learn how to best order a set of documents, e.g., a set of search results

The web and its challenges

• Unusual and diverse documents

• Unusual and diverse users, queries, information needs

• Beyond terms, exploit ideas from social networks

• How do search engines work?
And how can we make them better?

More sophisticated information retrieval

• Cross-language information retrieval

• Summarization

• Text mining

Resources for today’s lecture

• Introduction to Information Retrieval, chapter 1

• Shakespeare:

• Try the neat browse by keyword sequence feature!

• Managing Gigabytes, chapter 3.2

• Modern Information Retrieval, chapter 8.2