2. Information Retrieval
For the MSc Computer Science Programme
Lecture 1
Introduction to Information Retrieval (Manning et al. 2007)
Chapter 1
Dell Zhang
Birkbeck, University of London

3. What is IR? IR is about search engines. Database System VS
Structured Data (Tables)
SQL Queries VS
Search Engine
Unstructured Data (Text)
Keyword Queries
Structured data has been the big commercial success (e.g., Oracle) but unstructured data is now becoming dominant in a large and increasing range of activities.

4. Search is Cool Text everywhere Much more than text
books, documents (ms-word, pdf, etc.), articles (journal, magazine, newspaper, etc.), Web pages, s, SMS, chat, …
Much more than text
music, photos, videos, …
Much more than the Web
personal
enterprise, institutional and domain-specific

5. Search is Cool
Not only search/finding, but also organization and mining
clustering
classification ……
For example, given thousaunds of CVs, …

6. Video: How Big Can You Think?
Search is Hot
Most people’s means of information access
In the 1990s: other people
Nowadays: Web search
“92% of Internet users say the Internet is a good place to go for getting everyday information” – (2004 Pew Internet Survey)
The Search Engine War
Google, Yahoo, Microsoft, …
Video: How Big Can You Think?

7. Data: Unstructured vs. Structured
in 1996

8. Data: Unstructured vs. Structured
in 2006

9. Free Textbook Online
Head of Yahoo! Research
C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press
information-retrieval-book.html
Don’t remember. Search!
Another reason for you to come to this module.

10. A Simple Search Engine http://www.rhymezone.com/shakespeare/
“to be, or not to be”
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?
Slow (for large corpora)
NOT Calpurnia is non-trivial
Other operations (e.g., find the word Romans near countrymen) not feasible
How do you search in a book?

11. Boolean Queries
Boolean Queries are queries using AND, OR and NOT together with query terms.
Each document is viewed as a set of words.
Precise: a document matches condition or not.
Primary commercial retrieval tool for 3 decades.
Professional searchers still like Boolean queries - you know exactly what you’re getting.
For example, .

12. Term-Document Incidence
1 if play contains word, 0 otherwise
Brutus AND Caesar but NOT Calpurnia

13. Incidence Vectors So we have a 0/1 vector for each term.
To answer the query
take the vectors for Brutus, Caesar and Calpurnia (complemented)  bitwise AND.
& & =

14. Query Results Antony and Cleopatra, Act III, Scene ii……
Agrippa [Aside to DOMITIUS ENOBARBUS]: Why, Enobarbus,
When Antony found Julius Caesar dead,
He cried almost to roaring; and he wept
When at Philippi he found Brutus slain.
Hamlet, Act III, Scene ii
…….
Lord Polonius: I did enact Julius Caesar I was killed i' the
Capitol; Brutus killed me.

15. Bigger Corpora Consider n = 1M documents each with about 1K terms.
Avg 6 bytes/term incl spaces/punctuation
6GB of data in the documents.
Say there are m = 500K distinct terms among these.

16. Bigger Corpora The 500K x 1M matrix has
half-a-trillion 0’s and 1’s,
but no more than one billion 1’s.
The matrix is extremely sparse.
Can’t build the matrix straightforwardly.
What’s a better representation?
Only record the 1 positions.
Why?

17. Inverted Index
For each term T, we must store a list of all documents that contain T.
Do we use an array or a list for this?
Brutus 2 4 8 16 32 64
128
Calpurnia 1 2 3 5 8 13 21 34
Caesar 13 16
What happens if the word Caesar is added to document 14?

18. Inverted Index Linked lists generally preferred to arrays
Dynamic space allocation
Insertion of terms into documents easy
Space overhead of pointers 2 4 8 16 32 64
128
Dictionary
Brutus
Calpurnia
Caesar 1 2 3 5 8 13 21 34 13 16
Postings
(sorted by docID)

19. Inverted Index - Construction
Tokenization
Token Stream
Friends
Romans
Countrymen
Linguistic Preprocessing
Modified Tokens
friend
roman
countryman
Indexing
Inverted Index 2 4 13 16 1
Documents
(to be indexed)
Friends, Romans, countrymen.

20. Linguistic Preprocessing
Case-folding
Often best to lower case everything, since users will use lowercase regardless of ‘correct’ capitalization.
Removal of stopwords
Very common words like the, of, to, etc.
List of stopwords (stop lists)
linguistic_utils/stop_words

21. Linguistic Preprocessing
Stemming
Reduce terms to their “roots” before indexing, e.g., automate(s), automatic, automation all reduced to automat.
Porter’s stemmer
Implementations in C, Java, Perl, Python, etc. ……

22. Indexing Input a sequence of (term, docID) pairs Doc 1 Doc 2
I did enact Julius
Caesar I was killed
i' the Capitol;
Brutus killed me.
So let it be with
Caesar. The noble
Brutus hath told you
Caesar was ambitious

23. Sort
by terms.
This is the core indexing step,

24. Merge Add multiple term entries in a single document.
frequency information.
Why?
Will discuss later.

25. Split
the result into
a dictionary file and a postings file.

26. The index we just built Where do we pay in storage?
How do we process a query?

27. Query Processing Consider processing the query: Brutus AND Caesar
Locate Brutus in the dictionary;
Retrieve its postings.
Locate Caesar in the dictionary;
“Merge” the two postings: 2 4 8 16 32 64
128
Brutus 1 2 3 5 8 13 21 34
Caesar

28. Query Processing - Merge
Walk through the two postings simultaneously, in time linear in the total number of postings entries. In other words, if the posting list lengths are x and y, the merge takes O(x+y) operations. 2 34
128 2 4 8 16 32 64 1 3 5 13 21 4 8 16 32 64
128
Brutus
Caesar 2 8 1 2 3 5 8 13 21 34
What’s crucial: postings must be sorted by docID.

29. Query Processing - Exercise
Adapt the merge algorithm for the queries:
Brutus AND NOT Caesar
Brutus OR NOT Caesar
Can we still run through the merge in time O(x+y)?

30. Query Processing - Exercise
What about an arbitrary Boolean formula?
(Brutus OR Caesar) AND NOT (Antony OR Cleopatra)
Can we always merge in “linear” time?
The time complexity is linear in what?
Can we do better?

31. Query Processing - Exercise
Extend the merge algorithm 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, then each query term appears only once in the query.

32. Take Home Messages IR is about search engines. Free textbook online!
Search is hot! Search is cool!
Free textbook online!
Inverted Index
dictionary + postings
Boolean Search
query processing