Intelligent Information Retrieval (and Web Search)

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Intelligent Information Retrieval(and Web Search)

• Professor Celso A A Kaestner, PhD.
• Brazil

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Sitewww.dainf.ct.utfpr.edu.br/kaestner/Konstanz
/iir.htm
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Introduction
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Introduction Information Retrieval

• IR representation, storage, organization of, and
  access to information items
• Focus is on the user information need
• User information need
• Find all docs containing information on college
  football teams which (1) are maintained by an
  university and (2) participate in the national
  tournament.
• Emphasis is on the retrieval of information (not
  data).

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Data retrieval x Information retrieval

• Data Retrieval
• which docs. contain a set of keywords?
• well defined semantics
• a single erroneous object implies failure!
• Information Retrieval (IR)
• information about a subject or topic
• semantics is frequently loose
• small errors are tolerated.
• IR system
• interpret contents of information items
• generate a ranking which reflects relevance
• notion of relevance is most important.

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Information Retrieval (IR)

• The indexing and retrieval of textual documents.
• Searching for pages on the World Wide Web is the
  most recent killer app.
• Concerned firstly with retrieving relevant
  documents to a query.
• Concerned secondly with retrieving from large
  sets of documents efficiently.

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Typical IR Task

• Given
• A corpus of textual natural-language documents.
• A user query in the form of a textual string.
• Find
• A ranked set of documents that are relevant to
  the query.

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IR System
IR System
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Relevance

• Relevance is a subjective judgment and may
  include
• Being on the proper subject.
• Being timely (recent information).
• Being authoritative (from a trusted source).
• Satisfying the goals of the user and his/her
  intended use of the information (information
  need).

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Keyword Search

• Simplest notion of relevance is that the query
  string appears verbatim in the document.
• Slightly less strict notion is that the words in
  the query appear frequently in the document, in
  any order (bag of words).

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Problems with Keywords

• May not retrieve relevant documents that include
  synonymous terms.
• restaurant vs. café
• PRC vs. China
• May retrieve irrelevant documents that include
  ambiguous terms.
• bat (baseball vs. mammal)
• Apple (company vs. fruit)
• bit (unit of data vs. act of eating)

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Beyond Keywords

• We will cover the basics of keyword-based IR,
  but
• We will focus on extensions and recent
  developments that go beyond keywords.
• We will cover the basics of building an efficient
  IR system, but
• We will focus on basic capabilities and
  algorithms rather than systems issues that allow
  scaling to industrial size databases.

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Intelligent IR

• Taking into account the meaning of the words
  used.
• Taking into account the order of words in the
  query.
• Adapting to the user based on direct or indirect
  feedback.
• Taking into account the authority of the source.

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IR System Architecture
User Interface
Text
User Need
Text Operations
Logical View
Database Manager
Indexing
Query Operations
User Feedback
Inverted file
Searching
Index
Query
Text Database
Ranked Docs
Retrieved Docs
Ranking
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IR System Components

• Text Operations forms index words (tokens).
• Standardization (caps )
• Stopword removal
• Stemming
• Indexing constructs an inverted index of word to
  document pointers.
• Searching retrieves documents that contain a
  given query token from the inverted index.
• Ranking scores all retrieved documents according
  to a relevance metric.

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IR System Components (continued)

• 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.

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IR and the Web

• IR at the center of the stage
• Advent of the Web changed this perception once
  and for all
• universal repository of knowledge
• free (low cost) universal access
• no central editorial board
• many problems though IR seen as key to finding
  the solutions!

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IR and the Web

• And more
• Most of the human task employ the treatment of
  information in textual and/ or graphic form
  (Lyman, 2003)
• How Much Information project (Berkeley)
• www.sims.berkeley.edu/how-much-info-2003.
• Each person generates 800 Mbytes / year.

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IR and the Web

• In 2002 5 Exabytes of new information
• Magnetic media (HDs) 92
• Films 7
• Print material 0,01
• Optical media 0,002.
• 5 Exabytes 5 million Terabytes
  5.000.000.000.000.000.000 bytes
• 2 times the amount of 1999, given an increasing
  rate of 30 / year.

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IR and the Web

• Information flow - radio, TV, Internet
• 18 Exabytes of new information in 2002
• 3,5 times of the amount stored
• Telephone lines (and cell phones) 98
• 320 million hours of radio and TV transmissions,
  with 70 million new hours, with 81 Gigabytes of
  texts.

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IR and the Web

• Email
• 31 billion of e-mails / year 400.000 Tbytes of
  new information
• The Internet (Web)
• 170 Tbytes of information 17 times the printed
  content of the US Library of Congress.

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IR and the Web

• Search sites
• Yahoo, Google, etc. the 1st option of
  access for the users
• A typical Internet user 11 h 20 m / month
• Access to the desired information 1 / 3 of the
  period
• The user is obliged to verify if the received
  information is the desired one, and several times
  is impossible to recover the information needed.

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IR and the Web

• Information Glut or Information Overload is the
  main challenge to be surpassed by automatic text
  treatment systems.

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Web Search

• Application of IR to HTML documents on the World
  Wide Web.
• Differences
• Must assemble document corpus by spidering the
  web.
• Can exploit the structural layout information in
  HTML (XML).
• Documents change uncontrollably.
• Can exploit the link structure of the web.

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Web Search System
IR System
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Other IR-Related Tasks

• Automated document categorization
• Information filtering (spam filtering)
• Information routing
• Automated document clustering
• Recommending information or products
• Information extraction
• Information integration
• Question answering

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History of IR

• 1960-70s
• Initial exploration of text retrieval systems
  for small corpora of scientific abstracts, and
  law and business documents.
• Development of the basic Boolean and vector-space
  models of retrieval.
• Prof. Salton and his students at Cornell
  University are the leading researchers in the
  area.

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IR History Continued

• 1980s
• Large document database systems, many run by
  companies
• Lexis-Nexis
• Dialog
• MEDLINE

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IR History Continued

• 1990s
• Searching FTPable documents on the Internet
• Archie
• WAIS
• Searching the World Wide Web
• Lycos
• Yahoo
• Altavista

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IR History Continued

• 1990s continued
• Organized Competitions
• NIST TREC
• Recommender Systems
• Ringo
• Amazon
• NetPerceptions
• Automated Text Categorization Clustering

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Recent IR History

• 2000s
• Link analysis for Web Search
• Google
• Automated Information Extraction
• Whizbang
• Fetch
• Burning Glass
• Question Answering
• TREC Q/A track

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Recent IR History

• 2000s continued
• Multimedia IR
• Image
• Video
• Audio and music
• Cross-Language IR
• DARPA Tides
• Document Summarization

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Related Areas

• Database Management
• Library and Information Science
• Artificial Intelligence
• Natural Language Processing
• Machine Learning

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Database Management

• Focused on structured data stored in relational
  tables rather than free-form text.
• Focused on efficient processing of well-defined
  queries in a formal language (SQL).
• Clearer semantics for both data and queries.
• Recent move towards semi-structured data (XML)
  brings it closer to IR.

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Library and Information Science

• Focused on the human user aspects of information
  retrieval (human-computer interaction, user
  interface, visualization).
• Concerned with effective categorization of human
  knowledge.
• Concerned with citation analysis and
  bibliometrics (structure of information).
• Recent work on digital libraries brings it closer
  to CS IR.

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Artificial Intelligence

• Focused on the representation of knowledge,
  reasoning, and intelligent action.
• Formalisms for representing knowledge and
  queries
• First-order Predicate Logic
• Bayesian Networks
• Others
• Recent work on web ontologies and intelligent
  information agents brings it closer to IR.

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Natural Language Processing

• Focused on the syntactic, semantic, and pragmatic
  analysis of natural language text and discourse.
• Ability to analyze syntax (phrase structure) and
  semantics could allow retrieval based on meaning
  rather than keywords.

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Natural Language ProcessingIR Directions

• Methods for determining the sense of an ambiguous
  word based on context (word sense
  disambiguation).
• Methods for identifying specific pieces of
  information in a document (information
  extraction).
• Methods for answering specific NL questions from
  document corpora.

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Machine Learning

• Focused on the development of computational
  systems that improve their performance with
  experience.
• Automated classification of examples based on
  learning concepts from labeled training examples
  (supervised learning).
• Automated methods for clustering unlabeled
  examples into meaningful groups (unsupervised
  learning).

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Machine LearningIR Directions

• Text Categorization
• Automatic hierarchical classification (Yahoo).
• Adaptive filtering/routing/recommending.
• Automated spam filtering.
• Text Clustering
• Clustering of IR query results.
• Automatic formation of hierarchies (Yahoo).
• Learning for Information Extraction
• Text Mining
• Text Summarization