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Intelligent Information Retrieval(and Web Search)
Professor Celso A A Kaestner, PhD.
Brazil
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Site:www.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
IRSystem
Query String
Documentcorpus
RankedDocuments
1. Doc12. Doc23. Doc3 . .
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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 system’s 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
TextDatabase
DatabaseManager
Indexing
Index
QueryOperations
Searching
RankingRanked
Docs
UserFeedback
Text Operations
User Interface
RetrievedDocs
UserNeed
Text
Query
Logical View
Inverted file
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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 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!
IR and the Web
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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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In 2002: 5 Exabytes of new information;• Magnetic media (HD’s): 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.
IR and the Web
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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.
IR and the Web
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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.
IR and the Web
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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.
IR and the Web
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• Information Glut or Information Overload: is the main challenge to be surpassed by automatic text treatment systems.
IR and the Web
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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
Query String
IRSystem
RankedDocuments
1. Page12. Page23. Page3 . .
Documentcorpus
Web Spider
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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
• 1960-70’s:– 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
• 1980’s:– Large document database systems, many
run by companies:• Lexis-Nexis• Dialog• MEDLINE
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IR History Continued
• 1990’s:– Searching FTPable documents on the
Internet• Archie• WAIS
– Searching the World Wide Web• Lycos• Yahoo• Altavista
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IR History Continued
• 1990’s continued:– Organized Competitions
• NIST TREC
– Recommender Systems• Ringo• Amazon• NetPerceptions
– Automated Text Categorization & Clustering
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Recent IR History
• 2000’s– Link analysis for Web Search
– Automated Information Extraction• Whizbang• Fetch• Burning Glass
– Question Answering• TREC Q/A track
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Recent IR History
• 2000’s 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 Processing:IR 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 Learning:IR 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