Text Information Retrieval and Applications

1
Text Information Retrieval and Applications
Advanced Topics

• By J. H. Wang
• May 27, 2009

2
Outline

• Advanced Retrieval Technologies
• Cross-Language Information Retrieval
• Multimedia Information Retrieval
• Semantic Retrieval
• Applications to IR
• Advanced Google
• Meta Search
• Search Result Clustering

3
Advanced Retrieval Technologies

• Cross-Language Information Retrieval (CLIR)
• Multimedia IR (image, speech, music, video)
• Semantic retrieval (XML, Semantic Web)

4
Cross-Language Information Retrieval

• Cross Language Information Retrieval (CLIR) -- A
  technology enabling users to query in one
  language and retrieve relevant documents written
  or indexed in another language

5
Cross Language Web Search

• A technology enabling users to query in one
  language and retrieve relevant Web pages written
  or indexed in another language

6
Why Cross-Language?

• Source Global Reach (global-reach.biz/globstats)

7
Internet World Users by Language
8
Top Ten Languages Used in the Web
Source Internet World Stats (Mar. 31, 2009)

TOP TEN LANGUAGESIN THE INTERNET Internet Usersby Language InternetPenetrationby Language Growthin Internet( 2000 - 2008 ) Internet Users of Total World Populationfor this Language(2008 Estimate)
English 463,790,410 37.2 226.7 29.1 1,247,862,351
Chinese 321,361,613 23.5 894.8 20.1 1,365,138,028
Spanish 130,775,144 32.0 619.3 8.2 408,760,807
Japanese 94,000,000 73.8 99.7 5.9 127,288,419
French 73,609,362 17.8 503.4 4.6 414,043,695
Portuguese 72,555,800 29.7 857.7 4.5 244,080,690
German 65,243,673 67.7 135.5 4.1 96,402,666
Arabic 41,396,600 14.2 1,545.2 2.6 291,073,346
Russian 38,000,000 27.0 1,125.8 2.4 140,702,094
Korean 36,794,800 51.9 93.3 2.3 70,944,739
TOP 10 LANGUAGES 1,337,527,402 30.4 329.2 83.8 4,406,296,835
Rest of the Languages 258,742,706 11.2 424.5 16.2 2,303,732,235
WORLD TOTAL 1,596,270,108 23.8 342.2 100.0 6,710,029,070
Top Ten Languages Used in the Web( Number of Internet Users by Language )
More and more non-English users!
9
Web Content
More and more non-English pages
Source Network Wizards Internet Domain Survey
(Jan 99 )
10
Chart of Web Content (by Language)
Source Vilaweb.com, as quoted by eMarketer
(Feb. 2001)

• Total Web pages 313 B
• English 68.4
• Japanese 5.9
• German 5.8
• Chinese 3.9
• French 3.0
• Spanish 2.4
• Russian 1.9
• Italian 1.6
• Portuguese 1.4
• Korean 1.3
• Other 4.6

11
Language Percent of Public Sites

• English 72
• German 7
• Japanese 6
• Spanish 3
• French 3
• Italian 2
• Dutch 2
• Chinese 2
• Korean 1
• Portuguese 1
• Russian 1
• Polish 1

Source OCLC, 2002
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Web Users and Pages (10 years ago)
Challenge of Scalability !
Total Users 800MChinese Users 110M Including
87M (CN), 4.9M (HK), 11.6M (TW), 2.9M (MY), 2.14M
(SG), 1.5M (US), and others. Source Global
Reach, 2004
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Number of Chinese Web Pages
10,030,000,000 pages
Scalability Problem !
14
Number of Web Pages
The worlds largest search engine ?
Billions Of Textual Documents IndexedDecember
1995-September 2003
Search Engine Reported Size Page Depth
Google 8.1 billion 101K
MSN 5.0 billion 150K
Yahoo 4.2 billion (estimate) 500K
Ask Jeeves 2.5 billion 101K
KEY GGGoogle, ATWAllTheWeb, INKInktomi,
TMATeoma, AVAltaVista. Source Search Engine
Watch (Nov. 2004)
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Number of Web Pages

• Estimated size
• Web pages in the world 19.2 billion pages
  (indexed by Yahoo as of August 2005)
• Websites in the world 70,392,567 websites
  (indexed by Netcraft as of August 2005)
• Web pages per website 273 (rounding to the
  nearest whole number)
• Updated estimate
• 231,510,169 distinct websites (as found by the
  Netcraft Web Server Survey in April 2009)
• 63.2 billion

Source http//news.netcraft.com/archives/web_ser
ver_survey.html
Source http//www.boutell.com/newfaq/misc/sizeof
web.html
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Number of Web Pages

• 1 trillion unique URLs (We knew the web was big,
  by Jesse Alpert Nissan Hajaj, Software
  Engineers, Web Search Infrastructure Team, 25
  July 2008)
• 19,200,000,000 pages (Mayer, Tim, 8 August 2005,
  Our Blog is Growing Up And So Has Our Index)
• 320,000,000 pages (World Wide Web is 320 million
  and growing, BBC News Sci/Tech, 3 April 1998.)
• 1,000,000,000 pages (Internet. How much
  information? 2000. Regents of the University of
  California.)
• 800,000,000 pages (Maran, Ruth, and Paul
  Whitehead. "Web Pages." Internet and World Wide
  Web Simplified, 3rd ed. Foster City IDG Books
  Worldwide, 1999. )
• 8,034,000,000 pages (Miller, Colleen. web sites
  number of pages. NEC Research, IDC.)

Source http//hypertextbook.com/facts/2007/Loran
tLee.shtml
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Challenge of Cross-Language Web Search

• Existing CLIR systems mostly rely on bilingual
  dictionaries and dictionary lookup
• 81 of the search terms could not be obtained
  from common English-Chinese translation
  dictionaries

????? (CPU), ???? (E-commerce), ??????(PDA), ??
(Yahoo), ???? (NASA), ???? (Star War), ?????
(SARS),
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Challenge

• Existing CLIR systems mostly rely on bilingual
  dictionaries and dictionary lookup
• 81 of the search requests could not be obtained
  from common English-Chinese translation
  dictionaries
• How to find effective translations automatically
  for query terms not included in a dictionary ?

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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
Possible global use
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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
Need for CLIR services
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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
Query Translation
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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
Cost-ineffective to construct translation
dictionaries
Query Translation
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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
Query Translation
Taking the Web as online corpus to deal with
translation of unknown terms
?
Web
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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?????
English Query
Query Translation
National Palace Museum
?
Online Term Translation Suggestions
?
Web
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Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
English/Japanese/Korean Queries
?
Query Translation
?
Auto- generated Translation Lexicons
?
Web
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CLIR

• Conventional approach to query translation
• Parallel documents as the corpus
• Assume long queries
• Problems of CLIR in digital libraries
• No corpus for cross-lingual training
• Short queries
• ? Out-of-dictionary terms
• Ex proper nouns, new terminologies,

English Terminologies Chinese Translation
mechanical strain ????
viscous damping ????
Richard Feynman ??
Hyoplastic Left Heart Syndrome ?????????
NII Japan ????????
SARS ??????????
Extracorporeal Shock Wave Lithotripsy ????
Davinci ???
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Translation Lexicon Construction for CLIR

• To use the Web as the corpus for query
  translation
• Web mining techniques
• Anchor-text-based ACM TOIS 04, ACM TALIP 02
• Search-result-based JCDL 04
• To extract terms from real document collections
  as possible queries
• Term extraction method SIGIR 97

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Web Mining Approach to Term Translation Extraction
The Web
Source query
Anchor texts
Academia Sinica
LiveTrans Engine
Search results
Target translations
?????/???

• LiveTrans http//wkd.iis.sinica.edu.tw/LiveTrans/

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National Palace Museum vs. ?????Search-Result
Page
Noises

• Mixed-language characteristic in Chinese pages
• How to extract translation candidates?
• Which candidates to choose?

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Yahoo vs. ?? -- Anchor-Text Set

• Anchor text (link text)
• The descriptive text of a link on a Web page
• Anchor-text set
• A set of anchor texts pointing to the same page
  (URL)
• Multilingual translations
• Yahoo/??/??
• America/??/????
• Anchor-text-set corpus
• A collection of anchor-text sets

??-USA
Korea
Yahoo Search Engine
Yahoo! America
?????Yahoo!
http//www.yahoo.com
????
??????
Japan
Taiwan
China
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Term Translation Extraction from Different
Resources
WebSpider
Term Extraction
Search Engine
SimilarityEstimation
Source Query
Target Translation
National Palace Museum
???????, ??, ?????
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LiveTrans Cross-language Web Search
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More Examples
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More Examples
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Multimedia IR

• Different forms of information need
• Image retrieval
• Speech information retrieval
• Music information retrieval
• Video information retrieval

36
Image Retrieval

• Content-based
• Query by image content
• Query by example (????)
• Similarity in visual features
• Color, texture, shape,
• Relevance feedback
• Text-based
• Annotation

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Content-Based Image Retrieval (CBIR)

• Example systems
• CIRES (Content-based Image Retrieval System)
  http//amazon.ece.utexas.edu/qasim/research.htm
• SIMPLIcity http//www-db.stanford.edu/IMAGE/
• National Museum of History http//210.201.141.12/
  cgi-bin/cbir-query.cgi?tid-1

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Relevance Feedback (RF)
Source Dr. Cheng
Image
Similar images (no RF)
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Similar Images Using Relevance Feedback
Image
Similar images using RF
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Automatic Image Annotation
Problem 1
Keywords?
Visual Similarity
polar bear ice snow
white bear snow tundra
polar bears snow fight
Image Banks with Annotations
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Spoken Document Retrieval

• Spoken document retrieval
• Indexing speech messages using speech recognition
• Retrieving relevant messages for a text/speech
  query
• Techniques
• Document Processing acoustic change detection,
  speech/non-speech detection, Mandarin/non-Mandarin
  detection, story segmentation, speaker
  recognition/clustering
• Speech Recognition
• Indexing/Retrieval

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SoVideo
43
Music Information Retrieval

• Finding a song by similar melody
• Query by singing
• Query by humming
• Singer identification
• Background noise
• Singer voice model

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Video Information Retrieval

• Difference with CBIR
• Temporal information
• Structural organization
• Complexity of querying system
• Techniques
• Video segmentation
• Keyframe identification

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Semantic Retrieval

• HTML vs. XML
• Semantic Web (Agent, Ontology, RDF)

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Common Language of the Web

• HTML
• Link Pi ? Pj
• URL (URI), anchor text
• Part-of

National Taiwan University
http//www.ntu.edu.tw/
NTU
47
Link Analysis Hubs Authorities in PageRank
48
Current Web Search

• Keyword-based search (e.g., Google)
• Full text indexing
• Page authority (link analysis)
• Page popularity (query log and users click)
• Problems
• Not specific
• Data in pages have no semantic annotations
• Yo-yo Mas most recent CD
• No topic disambiguation
• Documents with different topics mix together
• Yo-yo Mas CDs, concerts, biography, gossips,

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Search on Semantic Web

• Metadata search
• To increase precision and flexibility
• Topic-based search
• To help contextualize queries and overlay results
  in terms of a knowledge base

50
XML (Extensible Markup Language)

• More flexible tags
• DTD (Data Type Definition)
• Definition of the tags

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XML Search

• XML Text Search Engines
• Amberfish (Etymon)
• X3 (X-cubed) (DocSoft)
• UltraSeek (Verity)
• XML Structured Query Engines
• Fxgrep
• Cheshire II (UC Berkeley)
• XML Query Languages
• XQuery (W3C XMLQuery)
• XQL
• XML-QL

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Semantic Web

• "The Semantic Web is an extension of the current
  Web in which information is given well-defined
  meaning, better enabling computers and people to
  work in cooperation." -- Tim Berners-Lee, James
  Hendler, Ora Lassila, The Semantic Web,
  Scientific American, May 2001

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Semantic Web
Agent
Agent
RDF
ontology
Agent
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Semantic Web

• RDF (Resource Description Framework)
• Common language
• Ontology
• Knowledge representation
• Agent

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Why Semantic Web?

• Standardizing knowledge sharing and reusability
  on the Web
• Interoperable (independent of devices and
  platforms)
• Machine readableenabling intelligent processing
  of information

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An Example of Semantic Relation
author
work
written by
publisher
publish
57
What is a Software Agent?

• A paradigm shift of information utilization from
  direct manipulation to indirect access and
  delegation
• A kind of middleware between information demand
  (client) and information supply (server)
• A software that has autonomous, personalized,
  adaptive, mobile, communicative, social, decision
  making abilities

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What is Ontology?

• An ontology is a formal and explicit
  specification of shared conceptualization of a
  domain of interest (T. Gruber)
• Formal semantics
• Consensus of terms
• Machine readable and processible
• Model of real world
• Domain specific

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What is Ontology?(2)

• Generalization of
• Entity relationship diagrams
• Object database schemas
• Taxonomies
• Thesauri
• Conceptualization contains phenomena like
• Concepts/classes/frames/entity types
• Constraints
• Axioms, rules

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Agents and Ontology

• Agents must have domain knowledge to solve
  domain-specific problems
• Agents must have common sharable ontology to
  communicate and share knowledge with each other
• The common sharable ontology must be represented
  in a standard format so that all software agents
  can understand and communicate

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Agents and Semantic Web

• Semantic Web provides the structure for
  meaningful content of Web pages, so that software
  agents roaming from page to page will carry out
  sophisticated tasks
• An agent coming to a clinics web page will know
  Dr. Henry works at the clinic on Monday,
  Wednesday and Friday without having the full
  intelligence to understand the text
• Assumption is Dr. Henry make the page using an
  off-the-shelf tool, as well as the resources
  listed on the Physical Therapy Associations site

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Knowledge Representation on the Web

• The challenge of the Web is to provide a language
  to express both data and rules for reasoning
  about the data meta-data that allows rules from
  any existing knowledge representation system to
  be exported onto the Web
• Adding logic to the Web means to use rules to
  make inference, choose actions and answer
  questions. The logic must be powerful enough but
  not too complicated for agents to consider a
  paradox

63
Language Layers on the Web
Trust
DAML-L (logic)
Declarative Languages OIL, DAMLOnt
PICS
DC
XHTML SMIL
RDF
XML
HTML
Semantic web infrastructure is built on RDF data
model
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Languages on the Web

• HTMLURL
• XMLDTD (Data Type Definition)
• RDFRDF schema

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Statements RDF

• The basic structure of RDF is object-attribute-va
  lue
• In terms of labeled graph O-A-gtV

A
O
V
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Semantic Web Search Engine

• Swoogle http//swoogle.umbc.edu/ CIKM 2004
• SHOE (Simple HTML Ontology Extensions)
  http//www.cs.umd.edu/projects/plus/SHOE/search/
• SWSE http//www.swse.org/
• http//www.semanticwebsearch.com/

67
Applications to IR

• Advanced Google
• Meta Search
• Search Result Clustering

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What do Users Really Want?

• Topic-based vs. keyword-based
• NTU
• How to improve current search engines?
• Resources about Search Engines
• Search Engine Watch http//searchenginewatch.com/
  
• Research Buzz http//researchbuzz.com/

69
Advanced Google

• Is Google good enough?
• NTU
• NTU university
• NTU university Singapore
• More and more Services
• Google Web, Image, News, Video, Google Desktop
  Search ,
• Google Groups, Gmail, Google Talk, Google
  Calendar,
• Google Mobile, Google SMS, Google Local,
• Google Print (Book Search), Google Maps, Google
  Earth,
• Google Scholar, Translate, Finance, Docs, Reader,
  
• More about Google Services
• http//www.google.com/options/
• Google Labs http//labs.google.com/

70
More Types of Document Search

• Google Web, Image, News, Groups, Desktop
  (Office, mail),
• Microsoft Lookout (mail)
• Yahoo Stata (mail), Adobe (PDF)

71
Searching Different Media

• Multimedia Search MP3, Blog, messenger, mobile,
  
• Baidu.com MP3, image, news,
• Singingfish.com (AOL) audio/video,
• GoFish.com audio, video, mobile, games
• AllTheWeb.com pictures, audio, video,
• Blog search engines
• Daypop, Bloogz, Waypath,
• A9.com (by Amazon)
• Books, movies,
• Bookmark, history, discover, diary
• Mobissimo.com
• Airfare search, hotel search
• Yahoo-OCLC toolbar library search
• Searching Open WorldCat (OCLC union catalog)

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Different Forms of Presentation

• Clusty.com (by Vivisimo)
• Clustering engine
• Snap.com (by Idealab)
• Sorting by popularity, satisfaction, Web
  popularity, Web satisfaction, domain,
• Alexa.com (by Amazon)
• Average user review ratings,
• Visualization
• TouchGraph Google Browser http//www.touchgraph.c
  om/TGGoogleBrowser.html
• Kartoo.com a visual meta search engine
• Girafa
• ConceptSpace
• LostGoggles (formerly MoreGoogle) thumbnail
  preview

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Focused Search Engines

• Scirus http//scirus.landingzone.nl
• For scientific information only
• Google Scholar http//scholar.google.com/
• For scholarly literature

74
Some Google Hacks and Searching Tricks

• References
• Tara Calishain and Rael Dornfest, Google Hacks,
  OReilly
• Kevin Hemenway and Tara Calishain, Spidering
  Hacks, OReilly
• http//douweosinga.com/projects/googlehacks
• Tara Calishain, Web Search Garage, Prentice
  Hall
• Chris Sherman, Google Power Unleash the Full
  Potential of Google, McGraw Hill

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Further Utilizing Google

• Google API http//www.google.com/apis/
• 1,000 automated queries per day
• Google Hacks
• Google Talk
• Word Color
• Google Battle
• Google Date
• Google Best Time to Visit
• Google Protocol

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Meta (Federated) Search

• To search simultaneously several individual
  search engines and their databases of web pages
• Ixquick, Metacrawler, Dogpile,
• Clustering meta-searchers
• Vivisimo, KillerInfo,
• Meta-search engines for deep digging
• SurfWax, Copernic Agent,

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Meta Search Engine
Web
SE1
MetaSearchEngine
SE2
User
SEn
78
Search Result Clustering

• Why search result clustering?
• Why is SRC different from document clustering?
• In assessment of algorithms quality
• Precision, recall vs. user-oriented, subjective
  assessment

79
Example of Search Result Clustering
National Taiwan University
NTU Hospital
NTU?
Nanyang Technological University, Singapore
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Example Clustering Search Engines

• Vivisimo.com
• Clusty.com
• WebClust.com
• KillerInfo.com
• InfoNetWare.com
• SnakeT (Snippet Aggregation for Knowledge
  ExTraction) http//roquefort.unipi.it/
• A hierarchical clustering engine for snippets
• Mooter.com

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Example on Vivisimo
82
Vivisimo (cont.)
83
Clusty.com
84
InfoNetWare.com
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Thanks for Your Attention!