Indexing Strategies for the Linguist

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Indexing Strategies for the Linguists Search
Engine

• Aaron Elkiss and Philip Resnik
• UMIACS

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Why a Linguists Search Engine?

• Goal for linguists Use naturally occurring data
  to support theories
• Bag of word searches not sufficient
• Structural searches of parse trees would be better

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Constituency Parse
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A Web Search Tool for the Ordinary Working
Linguist

• Database
• Must permit real-time interaction
• Must permit large-scale searches
• Must allow search on linguistic criteria
• Interface
• Must have linguist-friendly look and feel
• Must minimize learning/ramp-up time
• Must be reliable
• Must evolve with real use

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Querying Parse Trees

• Find all trees containing a particular subtree
• We use Query by Example to edit an example
  sentence
• to the structure were interested in

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Query Properties

• Typically concerned with structure near the
  leaves of the tree
• Relationship can be ancestorship rather than
  immediate dominance

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LSE Design Criteria

• Must permit arbitrary structural searches
• multiple branches with wildcards
• in realtime
• on a large collection of sentences
• 1GB scaling up to 10GB or more

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Existing Techniques

• Convert data to a relational model
• Streaming techniques (tgrep2 (Rohde), XSQ
  (Chawathe et al.))
• Index, but permit only simple searches
  (DataGuides Widom et al.)
• Indexing techniques work best with a simple schema

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Goals

• Must handle a dataset with a very large schema
• 17 million paths from root to terminal
• Xmark 1GB has 2.4 million
• Path lengths also longer in LSE
• Set of paths from root to preterminal fixed in
  Xmark, grows without bound in LSE
• Must handle queries with wildcards well
• Must retrieve all results (100 recall)

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Assumptions

• Indexing can be slow (overnight)
• Doesnt need to support online update
• Can overgenerate results
• lt 100 precision
• Use tgrep2 as a filter

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Baseline Solution

• VIST A dynamic index method for querying XML
  data by tree structures (Wang et al (IBM Watson),
  SIGMOD 2003)
• Suffix-tree based approach
• Indexes structure and content together
• Supports branching queries well

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Suffix Trees

• Index all suffixes of a given string

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Structure Encoded Sequences

• Represent each node in DFS order with the
  complete path from the root to the node
• One parse tree one document one structure
  encoded sequence

S1 S_S1 NP_S_S1 NNP_S_S1
Jared_NNP_NP_S_S1 VP_S_S1 VBD_S_S1
laughed_VBD_VP_S_S1
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VIST Trees

• Insert structure encoded sequences instead of
  suffixes of a string

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Node Identification

• (DFS order / node ID , number of descendants)
  (n, d)
• DFS order uniquely identifies a node
• with number of descendants, identifies which
  nodes are descendants of a given node
• can produce without using a lot of memory using
  perl and UNIX sort utility

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VIST Indexes

• Two Btree indexes using BerkeleyDB
• Structural Sequence Index
• Document Index

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Structural Sequence Index

• Structural Sequence Element ? (n, d)
• S1 ? (0,12)
• VP_S_S1 ? (5,2), (10,2)

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Document Index

• documents inserted at node ID of last element

7 ?
12 ?
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Search
Query

• Order of branches in query is important

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Recursion Base Case

• After the last branch of the query
• Retrieve documents with descendant node IDs

7 ?
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Peculiarities of VIST

• Precision is not 100!
• Query
• matches both these documents

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Problematic Query - Wildcards

• Wildcards can still be a problem
• Recursion isnt deep but can be very wide
• End up looking at same nodes over and over again
  with different wildcard instantiations from
  previous branches

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Problematic Query - Wildcards
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Problematic Query Common Terminal

• VISTs structural index actually stores
• terminal length root preterminal
• the 6 S1 S VP FRAG X DT
• to find instantiated prefixes of structural
  sequence elements
• Wed look for
• JJR 5 S1 S VP FRAG X

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Problematic Query Common Terminal

• To find structural sequence elements like
  the_DT_X_FRAG_ we have to look at every element
  with the terminal the
• 220284 for the_ vs. 121 for the_DT_X_frag_

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Solution Overview

• Ignore insufficiently selective query branches
• Reorder processing of query branches
• Different ordering for structural index
• Create in-memory tree for the query
• Memoization of nodes matching subtree of query

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Ignore query branches

• Generate statistics for each pair of tokens
• Calculate estimated selectivity of each branch
• Discard insufficiently selective branches
• Use tgrep2 as filter

Still problematic
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Reorder query branches

• Start processing with most selective branch
• Join to proceeding branches, then following
  branches

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Reorder structural index

• Store as
• terminal preterminal root
• the DT X FRAG VP S S1
• Immediately find paths with particular suffix
• Terminals occurring in similar contexts are
  clustered together

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Reorder structural index

• Now we have to look at every JJR_X_FRAG_ instead
  of just those with the same prefix as
  the_DT_X_FRAG_
• But well only do so once, and only keep those
  the_DT_X_FRAG_ and JJR_X_FRAG_ who have
  matching prefixes

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Create Query Tree

• Keep relevant instantiations of each branch in
  memory

S1__NP__robot robot_NN_NP_NP_S_SBAR_S_X_
X_S1 robot_NN_NP_NP_S_SBAR_VP_FRAG_S1
robot_NN_NP_NP_S_SBAR_VP_S_S_S1 S1__VP VP_S_
S1 _laughs
laughs_VBZ_VP_VP_S_SBAR_NP_PP_NP_PP
_us us_PRP_NP VP_VP_S_SBAR_NP_PP_
NP_PP_VP_S_S1 _laughs laughs_VBZ
_us us_PRP_NP
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Subtree Memoization

• Create sorted list of all nodes for a particular
  branch of the query

S1__NP__robot robot_NN_NP_NP_S_SBAR_S_X_X_S1
(1,15) (30,10) S1__VP VP_S_S1
_laughs
laughs_VBZ_VP_VP_S_SBAR_NP_PP_NP_PP
(5,5) VP_VP_S_SBAR_NP_PP_NP_PP_VP_S_S1
_laughs laughs_VBZ (20,0)
S1__VP__laughs (5,5) (20,0)
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Subtree Memoization

• Specifier for memoized list includes wildcard
  instantiations

S1__VP VP_S_S1 _laughs
laughs_VBZ_VP_VP_S_SBAR_NP_PP_NP_PP (5,5)
(10,0) _us
us_PRP_NP (6,0)
us_PRP_NP_NP (50,0) VP_VP_S_SBAR
_NP_PP_NP_PP_VP_S_S1 _laughs
laughs_VBZ (20,20) _us
us_PRP_NP (60,0)
S1__VP__us / VP_S_S1 (6,0)
(50,0)
S1__VP__us / VP_VP_S_SBAR_NP_PP_NP_PP_VP_S_S1
(60,0)
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Evaluation

• Original VIST scalability
• XMark
• LSE data

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Original VIST scalability
Random queries over a synthetic data set
From Haixun Wang, Sanghyun Park, Wei Fan, and
Philip S Yu. VIST A dynamic index method for
querying XML data by tree structures. In SIGMOD,
2003. http//citeseer.nj.nec.com/wang03vist.html
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Evaluation - VIST

• Scales extremely well for Xmark

• qn vs. qnc cached vs. non-cached
• Queries same form as XPath queries from
  original VIST paper
• Q1 /site//itemlocationUS/mail/datetext12/
  15/1999 (3.7s)
• Q2 /site//person//citytextPocatello (2.5s)
• Q3 //closed_auctionpersonperson1/datetex
  t12/15/1999 (4.1s)

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Evaluation - LSE

• Need more data

• Queries two forms of a real LSE query

Q1
Q2
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Evaluation Index Size
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Future Directions

• Reimplement this original VIST in C
• Scale up to 10gb
• Improved query planning
• Ranking efficient top-k results
• Investigate usefulness for structural search of
  HTML documents

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HTML Structural Search

• Similar properties to LSE data
• no fixed schema
• no maximum path depth
• Whole Web search probably not yet feasible

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Ranking efficient top-k results

• Assign score to possible result
• Closer to matrix level higher score?
• Look for results with highest score first

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Improved Query Planning

• Dynamic Ignorance
• choose whether to use a query branch based on
  wildcard instantiations
• Full reordering of query branches

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Acknowledgments

• Philip Resnik, of course!
• Saurabh Khandelwal tree editor
• Doug Rohde tgrep2
• This work is supported by NSF ITR grant
  IIS0113641 .