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• ???,yhf_at_net.pku.edu.cn
• ?????????????
• 2004?12?24?_at_CERNET2004

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???? Web Search Engines

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Two service extremes
Browsing Services
Search Engine Services
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Web Pages
Bag of Words
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Two semantics extremes
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??????

• version 1.0 // version number
• url http//www.pku.edu.cn/ // URL
• origin http//www.somewhere.cn/ // original URL
• date Tue, 15 Apr 2003 081306 GMT // time of
  harvest
• ip 162.105.129.12 //
  IP address
• unzip-length 30233 // If
  included, the data must be compressed
• length 18133 // data length
• // a blank line
• XXXXXXXX // the followings are data part
• XXXXXXXX
• .
• XXXXXXXX // data end
• // insert a new line

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File Organizations (Indexes)

• Choices for accessing data during query
  evaluation
• Scan the entire collection
• Typical in early (batch) retrieval systems
• Computational and I/O costs are O(characters in
  collection)
• Practical for only small text collections
• Large memory systems make scanning feasible
• Use indexes for direct access
• Evaluation time O(query term occurrences in
  collection)
• Practical for large collections
• Many opportunities for optimization
• Hybrids Use small index, then scan a subset of
  the collection

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Indexes

• What should the index contain?
• Database systems index primary and secondarykeys
• This is the hybrid approach
• Index provides fast access to a subset of
  database records
• Scan subset to find solution set
• IR Problem
• Cannot predict keys that people will use in
  queries
• Every word in a document is a potential search
  term
• IR Solution Index by all keys (words) ?full
  text indexes

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Index Contents

• The contents depend upon the retrieval model
• Feature presence/absence
• Boolean
• Statistical (tf, df, ctf, doclen, maxtf)
• Often about 10 the size of the raw data,
  compressed
• Positional
• Feature location within document
• Granularities include word, sentence, paragraph,
  etc
• Coarse granularities are less precise, but take
  less space
• Word-level granularity about 20-30 the size of
  the raw data,compressed

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Indexes Implementation

• Common implementations of indexes
• Bitmaps
• Signature files
• Inverted files
• Common index components
• Dictionary (lexicon)
• Postings
• document ids
• word positions

No positional data indexed
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Inverted Files
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Inverted Files
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Word-Level Inverted File
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Inverted Search Algorithm

• Find query elements (terms) in the lexicon
• Retrieve postings for each lexicon entry
• Manipulate postings according to the retrieval
  model

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Word-Level Inverted File
lexicon
posting
Query 1.porridge pot (BOOL) 2.porridge
pot (BOOL) 3. porridge pot (VSM)
Answer
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A Brief history of Modern Information Retrieval

• In 1945, Vannevar Bush published "As We May
  Think" in the Atlantic monthly.
• In the 1960s, the SMART system by Gerard Salton
  and his students
• Cranfield evaluations done by Cyril Cleverdon
• The 1970s and 1980s saw many developments built
  on the advances of the 1960s.
• In 1992 with the inception of Text Retrieval
  Conference.
• The algorithms developed
• The algorithms developed in IR were employed for
  searching the Web from 1996.

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Clustering of SIGIR papers by topic vs. year
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Question answering
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Clustering
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Inverted files Implementations
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Message understanding TDT
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Filtering
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Hypertext IR, Multiple evidence
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Probabilistic Language models
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Distributed IR
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Evaluation
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Topic distillation Linkage retrieval
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Text categorisation
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Document summarisation
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Cross lingual
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???????????

• CIIR, University of Massachusetts
• LTI, Carnegie Mellon University
• The Stanford University DB Group
• Microsoft Research Asia
• TREC
• ????, ?????, ???

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Lemur??

• http//www-2.cs.cmu.edu/lemur/

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Lemur Toolkit

• ?????LM?IR???research system
• ad hoc , distributed retrieval, cross-language
  IR, summarization, filtering, and classification
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• ??Simple Language Model
• ????Language Model????????????
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• C and C
• Unix / Windows
• Current Version 3.1

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MRA Towards Next Generation Web Search

• From Pages to Blocks
• Analyze the Web at finer granularity
• From Surface Web to Deep Web
• Unleash the huge assets of high-value information
• From Unstructure to Structure
• Provide well organized results
• From relevance to intelligence
• Contribute knowledge discovery with search
• From Desktop Search to Mobile Search
• Bridge physical world search to digital world
  search

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The Stanford Univ. DB Group

• WebBase
• Crawling, storage, indexing, and querying of
  large collections of Web pages.
• Digital Libraries
• Infrastructure and services for creating,
  disseminating, sharing and managing information

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TREC Conference

• Established in 1992 to evaluate large-scale IR
• Retrieving documents from a gigabyte collection
• Has run continuously since then
• TREC 2004(13th) meeting is in November
• Run by NISTs Information Access Division
• Probably most well known IR evaluation setting
• Started with 25 participating organizations in
  1992 evaluation
• In 2003, there were 93 groups from 22 different
  countries
• Proceedings available on-line (http//trec.nist.go
  v )
• Overview of TREC 2003 at http//trec.nist.gov/pubs
  /trec12/papers/OVERVIEW.12.pdf

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TREC General Format

• TREC consists of IR research tracks
• Ad hoc, routing, confusion ( scanned documents,
  speech recognition ), video, filtering,
  multilingual ( cross-language, Spanish, Chinese
  ), question answering, novelty, high precision,
  interactive, Web, database merging, NLP,
• Each track works on roughly the same model
• November track approved by TREC community
• Winter tracks members finalize format for track
• Spring researchers train system based on
  specification
• Summer researchers carry out format evaluation
• Usually a blind evaluation research do not
  know answer
• Fall NIST carries out evaluation
• November Group meeting (TREC) to find out
• How well your site did
• How others tackled the program
• Many tracks are run by volunteers outside of NIST
  (e.g. Web)
• Coopetition model of evaluation
• Successful approaches generally adopted in next
  cycle

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TREC Tracks
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Summary of VLC/Web Track evaluation 1996 - 2003
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Tianwang Group _at_PKU
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http//www.infomall.cn/
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??2004-12-20??????????
2.5/8.8 28.4
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TEAM NAME TD-RUNS NPHP-RUNS
??????APEX??? APEX 5 5
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TRS?? TRS 5 2
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??????????????????? SCUTDB 5 5
?????? WLL 1

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Vector Space Model

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  ,? (?????tf,idf??)

BACK
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Query Answer

• 1.porridge pot (BOOL)
• d2
• 2.porridge pot (BOOL)
• null
• 3. porridge pot (VSM)
• d2 gt d1gtd5
• Next page?

BACK
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CIIR-Center for Intelligent Information Retrieval
_at_UMASS

• One of the leading research groups in IR
• improving the probabilistic models,
• first description of a retrieval system based on
  statistical language models.
• introduced and improved a number of techniques
  for text and query representation
• automatically representing databases and
  combining local searches for DIR
• first high capacity probabilistic filtering
  architecture
• define and evaluate the first versions of event
  detection and tracking software
• earliest research on ranking and representation
  techniques for Asian languages
• first approaches to information extraction that
  emphasized learning
• novel techniques for indexing images and video

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CIIR cont.

• Research
• more than 500 journal and refereed conference
  papers over the past 12 years (52 submissions in
  2003).
• industrial and government collaboration
• INQUERY
• licensed our software to nearly 300 sites
• Education
• 20 Ph.D.s , 29 M.S.
• 123/145, 34/4 graduate/undergraduate

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CIIR cont.

• Personnel
• Faculty 4 (W. BRUCE CROFT)
• Technical personel 10
• Graduate student 34/10
• Groups
• IESLInformation Extraction and Synthesis
  Laboratory
• IR Information Retrieval Laboratory
• MIR Multimedia Indexing and Retrieval Laboratory
• The CIIR is currently concentrating on the
  unsolved long-term research problems that
  underlie effective information retrieval
• text representation,
• query acquisition,
• retrieval models

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LTI Language Technologies Institue _at_CMU

• Machine Translation, Natural Language Processing,
  Speech, and Information Retrieval
• IR Projects (Jamie Callan and Yiming Yang )
• Adaptive Information Filtering
• Distributed Information Retrieval / Federated
  Search
• Email Classification and Prioritization
• Minerva Web Mining for Question Answering
• MuchMore Translingual Information Retrieval
• JAVELIN Open-Domain Question Answering

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