Block-based Web Search

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Block-based Web Search

• Deng Cai1, Shipeng Yu2, Ji-Rong Wen and
  Wei-Ying Ma
• Microsoft Research Asia
• 1Tsinghua University
• 2University of Munich

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Problems in Traditional IR

• Term-Document Irrelevance Problem
• Noisy terms
• Multiple topics
• Variant Document Length Problem
• Length normalization is important
• Passage Retrieval in traditional IR
• Partition the document to several passages
• Solve the problem in some sense
• Has three types of passages discourse, semantic,
  window
• Fixed-window passage is shown to be robust

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Problems in Web IR

• Noisy information
• Navigation
• Decoration
• Interaction
• Multiple topics
• May contain text as well as images or links

Noisy Information
Multiple Topics
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Problems in Web IR (Cont.)

• Variant Document Length Problem
• Conclusion in web IR all the problems of
  traditional IR remain and are more severe!

TREC-24 TREC-45 WT10g .GOV
Number of doc 524,929 556,077 1,692,096 1,247,753
Text size (Mb) 2,059 2,134 10,190 18,100
Median length (Kb) 2.5 2.5 3.3 7.5
Average length (Kb) 4.0 3.9 6.3 15.2
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Challenges in Web IR

• New characteristics of web pages
• Two-Dimensional
• Logical Structure
• Visual Layout
• Presentation
• Page segmentation methods can be achieved
• Obtain blocks from web pages
• Block-based web search is possible

Font Size
Color
Space
Font Style
Separator
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Outline

• Motivation
• Page segmentation approaches
• Web search using page segmentation
• Block Retrieval
• Block-level Query Expansion
• Experiments and Discussions
• Conclusion

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Web Page Segmentation Approaches
Web Page Segmentation FixedPS DomPS VIPS CombPS
Passage Retrieval Window Discourse Semantic Semantic Window

• Fixed-length approach (FixedPS)
• Traditional window-based passage retrieval
• DOM-based approach (DomPS)
• Like the natural paragraph in traditional passage
  retrieval
• Vision-based Web Page Segmentation (VIPS)
• Achieve a semantic partition to some extent
• Combined Approach (CombPS)
• Combined VIPS Fixed-length

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Fixed-length Page Segmentation (FixedPS)

• A block contains words of fixed-length
• Traditional window-based methods can be applied
• Approaches
• Overlapped windows (e.g. Callan, SIGIR94)
• Arbitrary passages of varying length (e.g.
  Kaszkiel et al, SIGIR97)
• Results
• A simple but robust approach
• Do not consider semantic information

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DOM-based Page Segmentation (DomPS)

• Rely on the DOM structure to partition the page
• DOM Document-Object Model
• Current approaches
• Only base on tags (e.g. Crivellari et al, TREC 9)
• Combine tags with contents and links (e.g.
  Chakrabarti et al, SIGIR01)
• Results
• Similar to discourse in passage retrieval
• DOM represents only part of the semantic
  structure
• Imprecise content structure

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VIPS Algorithm

• Motivation
• Topics can be distinguished with visual cues in
  many cases
• Utilize the two-dimensional structure of web
  pages
• Goal
• Extract the semantic structure of a web page to
  some extent, based on its visual presentation
• Procedure
• Top-down partition the web page based on the
  separators
• Result
• A tree structure, each node in the tree
  corresponds to a block in the page
• Each node will be assigned a value (Degree of
  Coherence) to indicate how coherent of the
  content in the block based on visual perception

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VIPS An Example
Microsoft Technical Report MSR-TR-2003-79
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Combined Approach (CombPS)

• VIPS solves the problems of noisy information and
  multi-topics
• FixedPS can deal with the variant document length
  problem
• Combine these two
• Partition the web page
• using VIPS
• Divide the blocks
• containing more words
• than pre-defined
• window length

Block length after segment 50,000 pages using
VIPS chosen from the WT10g data set
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Web Page Segmentation Summarization
Web Page Segmentation FixedPS DomPS VIPS CombPS
Passage Retrieval Window Discourse Semantic Semantic Window

• Fixed-length approach (FixedPS)
• traditional passage retrieval
• DOM-based approach (DomPS)
• Like the natural paragraph in traditional passage
  retrieval
• Vision-based Web Page Segmentation (VIPS)
• Achieve a semantic partition to some extent
• Combined Approach (CombPS)
• Combined VIPS Fixed-length

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Outline

• Motivation
• Page segmentation approaches
• Web search using page segmentation
• Block Retrieval
• Block-level Query Expansion
• Experiments and Discussions
• Conclusion

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Block Retrieval

• Similar to traditional passage retrieval
• Retrieve blocks instead of full documents
• Combine the relevance of blocks with relevance of
  documents
• Goal
• Verify if page segmentation can deal with both
  the length normalization and multiple-topic
  problems

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Block-level Query Expansion

• Similar to passage-level pseudo-relevance
  feedback
• Expansion terms are selected from top blocks
  instead of top documents
• Goal
• Testify if page segmentation can benefit the
  selection of query terms through increasing term
  correlations within a block, and thus improve the
  final performance

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Outline

• Motivation
• Page segmentation approaches
• Web search using page segmentation
• Block Retrieval
• Block-level Query Expansion
• Experiments and Discussions
• Conclusion

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Experiments

• Methodology
• Fixed-length window approach (FixedPS)
• Overlapped window with size of 200 words
• DOM-based approach (DomPS)
• Iterate the DOM tree for some structural tags
• A block is constructed and identified by such
  leaf tag
• Free text between two tags is treated as a
  special block
• Vision-based approach (VIPS)
• The permitted degree of coherence is set to 0.6
• All the leaf nodes are extracted as visual blocks
• The combined approach (CombPS)
• VIPS then FixedPS
• Full document approach (FullDoc)
• No segmentation is performed

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Experiments (Cont.)

• Dataset
• TREC 2001 Web Track
• WT10g corpus (1.69 million pages), crawled at
  1997
• 50 queries (topics 501-550)
• TREC 2002 Web Track
• .GOV corpus (1.25 million pages), crawled at 2002
• 49 queries (topics 551-560)
• Retrieval System
• Okapi, with weighting function BM2500
• Preprocessing
• Standard stop-word list
• Do not use stemming and phrase information
• Tune parameters in BM2500 to achieve best
  baselines
• Evaluation criteria P_at_10

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Experiments on Block Retrieval

• Steps
• Do original document retrieval
• Obtain a document rank DR
• Analyze top N (1000 here) documents to get a
  block set
• Do block retrieval on the block set (same as Step
  1 but replace the document with block)
• Obtain a block rank BR
• Documents are re-ranked by the single-best block
  in each document
• Combine the BR and DR to get a new rank of
  document
• is the tuning parameter

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Block Retrieval on TREC 2001 and TREC 2002 (P_at_10)
Page Segmentation Baseline BR only BR DR best
DomPS 0.2286 0.1571 0.2286
FixedPS 0.2286 0.1776 0.2317
VIPS 0.2286 0.2163 0.2408
CombPS 0.2286 0.1939 0.2379
Page Segmentation Baseline BR only BR DR best
DomPS 0.312 0.252 0.322
FixedPS 0.312 0.304 0.326
VIPS 0.312 0.316 0.328
CombPS 0.312 0.326 0.338
Result on TREC 2002 (P_at_10)
Result on TREC 2001 (P_at_10)
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Experiments on Block-level Query Expansion

• Steps
• Same steps as block retrieval
• Do original document retrieval to get DR
• Analyze top N (1000 here) documents to get a
  block set
• Do block retrieval on the block set to get BR
• Select some expansion terms based on top blocks
• 10 expansion terms in our experiments
• Number of top blocks is a tuning parameter
• Document retrieval with the expanded query
• Modify the term weights before final retrieval

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Query Expansion on TREC 2001 and TREC 2002 (P_at_10)
Page Segmentation Baseline Query Expansion (best) Query Expansion (best)
Page Segmentation Baseline P_at_10 Improvement
FullDoc 0.2286 0.2082 -8.9
DomPS 0.2286 0.2224 -2.7
FixedPS 0.2286 0.2327 1.8
VIPS 0.2286 0.2327 1.8
CombPS 0.2286 0.2388 4.5
Page Segmentation Baseline Query Expansion (best) Query Expansion (best)
Page Segmentation Baseline P_at_10 Improvement
FullDoc 0.312 0.326 4.5
DomPS 0.312 0.324 3.8
FixedPS 0.312 0.36 15.4
VIPS 0.312 0.362 16.0
CombPS 0.312 0.366 17.3
Result on TREC 2002 (P_at_10)
Result on TREC 2001 (P_at_10)
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Discussions

• FullDoc can only obtain a low and insignificant
  result
• The baseline is low, so many top ranked documents
  are actually irrelevant
• DomPS is not good and very unstable
• The segmentation is too detailed
• Semantic block can hardly be detected and
  expansion terms are not good
• FixedPS is stable and good
• Similar result as the case in traditional IR
• A window may miss the real semantic blocks
• VIPS is very good
• Top blocks usually have very good quality
• Length normalization is still a problem
• CombPS is almost the best method in all
  experiments
• More than just a tradeoff

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Outline

• Motivation
• Page segmentation approaches
• Web search using page segmentation
• Block Retrieval
• Block-level Query Expansion
• Experiments and Discussions
• Conclusion

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Conclusion

• Page segmentation is effective for improving web
  search
• Block Retrieval
• Block-level Query Expansion
• Plain-text retrieval ? Fixed-windows
  partition
• Web information retrieval ? Semantic
  partition (VIPS)
• Integrating both semantic and fixed-length
  properties (CombPS) could deal with all problems
  and achieve the best performance
• We believe that block-based web search can be
  very useful in real search engines, and can also
  be very easily combined with block-level link
  analysis

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Thanks!