ISP 433/533 Week 2

IR Models

Outline

• IR defined

• IR tasks

• IR processes

• Boolean model

• Break

• Vector space model

• Probabilistic model

User Information Needs

• Goal of IR

• Hard Problem– People have different and highly varied

needs for information– People often do not know what they want,

or may not be able to express it in a usable form

Some Definitions of IR

Salton (1989): “Information-retrieval systems process files of records and requests for information, and identify and retrieve from the files certain records in response to the information requests. The retrieval of particular records depends on the similarity between the records and the queries, which in turn is measured by comparing the values of certain attributes to records and information requests.”

Kowalski (1997): “An Information Retrieval System is a system that is capable of storage, retrieval, and maintenance of information. Information in this context can be composed of text (including numeric and date data), images, audio, video, and other multi-media objects).”

Examples of IR

• Conventional (library catalog). Search by keyword, title, author, etc.

• Text-based (Lexis-Nexis, Google, FAST).Search by keywords. Limited search using queries in natural language.

• Multimedia (QBIC, WebSeek, SaFe)Search by visual appearance (shapes, colors,… ).

• Question answering systems (AskJeeves, NSIR, Answerbus)Search in (restricted) natural language

Key Terms Used in IR

• QUERY: a representation of what the user is looking for - can be a list of words or a phrase.

• DOCUMENT: an information entity that the user wants to retrieve

• COLLECTION: a set of documents• INDEX: a representation of information that makes

querying easier• TERM: word or concept that appears in a document

or a query• RANKING: an ordering of the documents retrieved

that (hopefully) reflects the relevance of the documents to the user query

Basic IR Process

Information Need

Index Terms

doc

query

Rankingmatch

Docs

IR Task – ad hoc

Collection-relatively stable

Q2

Q3

Q1

Q4Q5

IR Task - filtering

Documents Stream

User 1Profile

User 2Profile

Docs Filteredfor User 2

Docs forUser 1

Process of IR

User Interface

Text operations

indexing DB Man.

Text Db

index

Queryoperations

Searching

Ranking

Document Process Steps

Classic IR models

• Each document represented by a set of representative keywords or index terms– Not all terms are equally useful for representing the

document contents: less frequent terms allow identifying a narrower set of documents

• Let – ki be an index term– dj be a document – wij is a weight associated with (ki,dj)

• The weight wij quantifies the importance of the index term for describing the document contents

Boolean Model

• Simple model based on set theory

• Queries specified as boolean expressions – precise semantics– neat formalism using boolean logic

– Eg. Queryx = ka (kb kc)

• Terms are either present or absent. Thus, wij {0,1}

Boolean Logic

• Named after logician/mathematician George Boole

• Logical Connectives: AND, OR, NOT– WARNING!

• INSPIRED BY, BUT NOT THE SAME AS, USUAL ENGLISH USAGE

AND: “Each thing must satisfy ALL conditions” OR : “Each thing must satisfy at least one

condition”NOT: “Each thing must NOT satisfy the given

condition”

Logical AND ()

(Set Intersection)

A B

is the set of things in common, i.e., in both sets A and B

A BAged Blind

A B(Aged, Blind People)

Logical OR ()

(Set Union)

A B

is the set of: things in either A, B or both.

A BAged Blind

A B (people that are either Aged or Blind or both)

Logical NOT ()

(Set Complement)

B

is the set of things outside the set B

B

(people who aren’t blind)

Blind

B

Example Combination

• A ( B)

B

(old people who aren’t blind)

Blind

A ( B)

AAged

Exercise

• D1 = “computer information retrieval”

• D2 = “computer retrieval”

• D3 = “information”

• D4 = “computer information”

• Q1 = “information retrieval”

• Q2 = “information computer”

Drawbacks of the Boolean Model

• Retrieval based on binary decision criteria with no notion of partial matching

• No ranking of the documents is provided (absence of a grading scale)

• Information need has to be translated into a Boolean expression which most users find awkward

• The Boolean queries formulated by the users are most often too simplistic

• As a consequence, the Boolean model frequently returns either too few or too many documents in response to a user query

• BREAK

Vector Model

• Non-binary weights provide consideration for partial matches

• These term weights are used to compute a degree of similarity between a query and each document

• Ranked set of documents provides for better matching

Vector Space

• Assume each term is independent from each other and each term defines a dimension

• T-dimensional space, where T is the number of terms

• In this space, queries and documents are represented as weighted vectors– Weight wiq >= 0 associated with the pair

(ki,q)– vec(dj) = (w1j, w2j, ..., wtj)– vec(q) = (w1q, w2q, ..., wtq)

Example Vector Space using term frequency

• D1 = “computer information retrieval”• D2 = “computer retrieval”• Q1 = “information, retrieval”

computer

information

retrieval

D1=(1, 1, 1)Q1=(0, 1, 1)

D2=(1, 0, 1)

Similarity Measure

• Sim(q,dj) = cos()= [vec(dj) vec(q)] / ( |dj| *

|q|) = [ wij * wiq] / (|dj| * |q|)

• Since wij > 0 and wiq > 0, 0 <= sim(q,dj) <=1

i

j

dj

q

Exercise

• D1 = “computer information retrieval”

• D2 = “computer retrieval”

• Q1 = “information, retrieval”

• Given the above query, rank the relevance of the above two documents using vector model

Pro and Con of Vector model

• Advantages:– term-weighting improves quality of the answer set– partial matching allows retrieval of docs that

approximate the query conditions– cosine ranking formula sorts documents according

to degree of similarity to the query

• Disadvantages:– assumes independence of index terms (??); not

clear that this is bad though

Probabilistic Model

• Given a user query, there is an ideal answer set

• Querying as specification of the properties of this ideal answer set (clustering)

• But, what are these properties? • Guess at the beginning what they could be

(i.e., guess initial description of ideal answer set)

• Improve by iteration

Probabilistic Ranking Principle

• Given a user query q and a document dj, the probabilistic model tries to estimate the probability that the user will find the document dj relevant

• sim(q, dj ) = P(dj relevant-to q) / P(dj non-relevant-to q)

Performance of Probabilistic Model

• Salton and Buckley did a series of experiments that indicate that, in general, the vector model outperforms the probabilistic model with general collections

• This seems also to be the view of the research community