Information Retrieval and Web Search

Text processing

Instructor: Rada MihalceaClass web page: http://www.cs.unt.edu/~rada/CSCE5300

(Note: This slide set was adapted from an IR course taught by Prof. Ray Mooney at UT Austin)

Slide 2

Last time

•Architecture of a classic IR system– Including main IR components

•Main IR models– Boolean– Vectorial– Probabilistic

Slide 3

IR System Architecture

TextDatabase

DatabaseManager

Indexing

Index

QueryOperations

Searching

RankingRanked

Docs

UserFeedback

Text Operations

User Interface

RetrievedDocs

UserNeed

Text

Query

Logical View

Inverted file

Slide 4

IR System Components

• Text Operations forms index words (tokens).– Tokenization– Stopword removal– Stemming

• Indexing constructs an inverted index of word to document pointers.– Mapping from keywords to document ids

I did enact JuliusCaesar I was killed

i' the Capitol; Brutus killed me.

Doc 1

So let it be withCaesar. The noble

Brutus hath told youCaesar was ambitious

Doc 2

Term Doc #I 1did 1enact 1julius 1caesar 1I 1was 1killed 1i' 1the 1capitol 1brutus 1killed 1me 1so 2let 2it 2be 2with 2caesar 2the 2noble 2brutus 2hath 2told 2you 2

caesar 2was 2ambitious 2

Slide 5

IR System Components

•Searching retrieves documents that contain a given query token from the inverted index.

•Ranking scores all retrieved documents according to a relevance metric.

•User Interface manages interaction with the user:– Query input and document output.– Relevance feedback.– Visualization of results.

•Query Operations transform the query to improve retrieval:– Query expansion using a thesaurus.– Query transformation using relevance feedback.

Slide 6

Today’s topics

•Text operations in IR systems– Tokenization– Stopword removal– Lemmatization– Stemming– In an IR system, text operations are applied on ???

•On metadata and markup languages – (if time permits)

Slide 7

Simple Tokenization

• Analyze text into a sequence of discrete tokens (words).

• Sometimes punctuation (e-mail), numbers (1999), and case (Republican vs. republican) can be a meaningful part of a token.– However, frequently they are not.

• Simplest approach is to ignore all numbers and punctuation and use only case-insensitive unbroken strings of alphabetic characters as tokens.

• More careful approach:– Separate ? ! ; : “ ‘ [ ] ( ) < > – Care with . - why? when?– Care with … ??

Slide 8

Punctuation

•Ne’er: use language-specific, handcrafted “locale” to normalize.

•State-of-the-art: break up hyphenated sequence.

•U.S.A. vs. USA - use locale.

•a.out

Slide 9

Numbers

•3/12/91

•Mar. 12, 1991

•55 B.C.

•B-52

•100.2.86.144– Generally, don’t index as text– Creation dates for docs

Slide 10

Case folding

•Reduce all letters to lower case– exception: upper case in mid-sentence

• e.g., General Motors• Fed vs. fed• SAIL vs. sail

Slide 11

Tokenizing HTML

•Should text in HTML commands not typically seen by the user be included as tokens?– Words appearing in URLs.– Words appearing in “meta text” of images.

•Simplest approach is to exclude all HTML tag information (between “<“ and “>”) from tokenization.

Slide 12

Stopwords

• It is typical to exclude high-frequency words (e.g. function words: “a”, “the”, “in”, “to”; pronouns: “I”, “he”, “she”, “it”).

• Stopwords are language dependent

• For efficiency, store strings for stopwords in a hashtable to recognize them in constant time. – Simple Perl hashtable for Perl-based implementations

• How to determine a list of stopwords?– For English? – may use existing lists of stopwords

• E.g. SMART’s commonword list (~ 400)• WordNet stopword list

– For Spanish? Bulgarian?

Slide 13

Lemmatization• Reduce inflectional/variant forms to base form

• Direct impact on VOCABULARY size

• E.g.,– am, are, is be

– car, cars, car's, cars' car

• the boy's cars are different colors the boy car be different color

• How to do this?– Need a list of grammatical rules + a list of irregular words

– Children child, spoken speak …

– Practical implementation: use WordNet’s morphstr function• Perl: WordNet::QueryData

– [ Digression: See “Words and Rules” by Steven Pinker• A theory on how human mind combines rules for regular words with

memorization of irregular forms ]

Slide 14

Stemming

•Reduce tokens to “root” form of words to recognize morphological variation.– “computer”, “computational”, “computation” all

reduced to same token “compute”

•Correct morphological analysis is language specific and can be complex.

•Stemming “blindly” strips off known affixes (prefixes and suffixes) in an iterative fashion.

for example compressed and compression are both accepted as equivalent to compress.

for exampl compres andcompres are both acceptas equival to compres.

Slide 15

Porter Stemmer

• Simple procedure for removing known affixes in English without using a dictionary.

• Can produce unusual stems that are not English words:– “computer”, “computational”, “computation” all

reduced to same token “comput”

• May conflate (reduce to the same token) words that are actually distinct.

• Not recognize all morphological derivations.

Slide 16

Typical rules in Porter

•sses ss

•ies i

•ational ate

•tional tion

•See class website for link to “official” Porter stemmer site– Provides Perl, C ready to use implementations

Slide 17

Porter Stemmer Errors

•Errors of “comission”:– organization, organ organ– police, policy polic– arm, army arm

•Errors of “omission”:– cylinder, cylindrical– create, creation– Europe, European

Slide 18

Other stemmers

•Other stemmers exist, e.g., Lovins stemmer http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm

•Single-pass, longest suffix removal (about 250 rules)

•Motivated by Linguistics as well as IR

•Full morphological analysis - modest benefits for retrieval

Slide 19

Stemming exercise

•Stemming procedure?

Slide 20

Remainder of today’s lecture

• On Metadata– Often included in Web pages– Hidden from the browser, but useful for indexing

• Information about a document that may not be a part of the document itself (data about data).

• Descriptive metadata is external to the meaning of the document:– Author– Title– Source (book, magazine, newspaper, journal)– Date– ISBN– Publisher– Length

Slide 21

Web Metadata

• META tag in HTML– <META NAME=“keywords” CONTENT=“pets,

cats, dogs”>

• META “HTTP-EQUIV” attribute allows server or browser to access information:– <META HTTP-EQUIV=“content-type”

CONTENT=“text/tml; charset=EUC-2”>

– <META HTTP-EQUIV=“expires” CONTENT=“Tue, 01 Jan 02”>

– <META HTTP-EQUIV=“creation-date” CONTENT=“23-Sep-01”>

Slide 22

RDF

• Resource Description Framework.

• XML compatible metadata format.

• New standard for web metadata.– Content description– Collection description– Privacy information– Intellectual property rights (e.g. copyright)– Content ratings– Digital signatures for authority

Slide 23

Markup Languages

•Language used to annotate documents with “tags” that indicate layout or semantic information.

•Most document languages (Word, RTF, Latex, HTML) primarily define layout.

•History of Generalized Markup Languages:

GML(1969) SGML (1985)

HTML (1993)

XML (1998)

Standard

HyperText

eXtensible

Slide 24

Basic SGML Document Syntax

•Blocks of text surrounded by start and end tags.– <tagname attribute=value attribute=value …>– </tagname>

•Tagged blocks can be nested.

•In HTML end tag is not always necessary, but in XML it is.

Slide 25

HTML

•Developed for hypertext on the web.– <a href=“http://www.unt.edu”>

•May include code such as Javascript in Dynamic HTML (DHTML).

•Separates layout somewhat by using style sheets (Cascade Style Sheets, CSS).

•However, primarily defines layout and formatting.

Slide 26

XML

• Like SGML, a metalanguage for defining specific document languages.

• Simplification of original SGML for the web promoted by WWW Consortium (W3C).

• Fully separates semantic information and layout.

• Provides structured data (such as a relational DB) in a document format.

• Replacement for an explicit database schema.

Slide 27

XML (cont’d)

• Allows programs to easily interpret information in a document, as opposed to HTML intended as layout language for formatting docs for human consumption.

• New tags are defined as needed.

• Structures can be nested arbitrarily deep.

• Separate (optional) Document Type Definition (DTD) defines tags and document grammar.

Slide 28

XML Example

<person>

<name> <firstname>John</firstname>

<middlename/>

<lastname>Doe</lastname>

</name>

<age> 38 </age>

</person>

<tag/> is shorthand for empty tag <tag></tag>

Tag names are case-sensitive (unlike HTML)

A tagged piece of text is called an element.

Slide 29

XML Example with Attributes

<product type=“food”>

<name language=“Spanish”>arroz con pollo</name>

<price currency=“peso”>2.30</price>

</product>

Attribute values must be strings enclosed in quotes.

For a given tag, an attribute name can only appear once.

Slide 30

Document Type Definition (DTD)

•Grammar or schema for defining the tags and structure of a particular document type.

•Allows defining structure of a document element using a regular expression.

•Expression defining an element can be recursive, allowing the expressive power of a context-free grammar.

Slide 31

DTD Example

<!DOCTYPE db [

<!ELEMENT db (person*)>

<!ELEMENT person (name,age,(parent | guardian)?>

<!ELEMENT name (#PCDATA)>

<!ELEMENT age (#PCDATA)>

<!ELEMENT parent (person)>

<!ELEMENT guardian (person)>

]>

*: 0 or more repetitions

?: 0 or 1 (optional)

| : alternation (or)

PCDATA: Parsed Character Data (may contain tags)

Slide 32

DTD (cont’d)

•Tag attributes are also defined:

<!ATTLIS name language CDATA #REQUIRED>

<!ATTLIS price currency CDATA #IMPLIED>

CDATA: Character data (string)

IMPLIED: Optional

•Can define DTD in a separate file:

<!DOCTYPE db SYSTEM “/u/doe/xml/db.dtd”>