Information Extraction with Tree Automata Induction

1
Information Extraction with Tree Automata
Induction

• Raymond Kosala1, Jan Van den Bussche2,
• Maurice Bruynooghe1, Hendrik Blockeel1
• 1 Katholieke Universiteit Leuven, Belgium
• 2 University of Limburg, Belgium

2
Outline

• Introduction
• information extraction (IE)
• grammatical inference
• Approach
• k-testable and g-testable algorithms
• Preliminary result
• Further work

3
IE from unstructured documents

• Extract certain fields of interest from a text
• Learner is trained with (positive) examples
• Each learner focuses on a single field marked
  with x

Company Name
Job Title
Requirement 1
Requirement 2
4
Grammatical inference

• ? finite alphabet
• Regular language L ? ?
• Given set of examples (pos. or neg.)
• Task infer a DFA compatible with examples
• Quality criterion
• Exact learning in the limit
• PAC
• etc.
• Large body of work

5
IE with grammatical inference

• Mark field x as special token
• Infer DFA for the language L
• S over (? ? x) the field to be extracted is
  marked by x
• Only positive examples

6
IE from structured documents

• Previous works learn string language
• XML or HTML data tree structured
• Natural extension is to learn a tree language
• x has a b-brother
• Extraction of a field can depend on structural
  context

7
Learning process

8
Testing process

9
Page examples
10
Why do we need the context?
-- tr -- td
-- td -- lastupdate
(CDATA) -- td
-- b --
12/4/98 -- td -- tr
-- td -- tr -- td
-- td --
organization (CDATA) -- td
-- b -- ABC
-- td -- tr -- td

• Not enough to differentiate the fields of
  interest that depend on the structural context.
• Can be chosen automatically.

11
Tree automata

• Ranked alphabet ? finite set of function symbols
  with arities.
• E.g. ? a(2), b(2), c(0)
• Tree ? ground term over ?
• a(c,a(b(c,c),b(c,c))) tree with depth 3
• Tree automaton M (?, Q, ?, F).
• ? is a set of transitions of the form v(q1, ,
  qn) ? q
• Where v ? ? , n is the arity of v , qi and q ? Q

12
Example

• Given an automaton M with the following
  transitions
• ?1 c ? q0
• ?2 a(q0 , q0) ? q0
• ?3 b(q0 , q0) ? accept
• M accepts a tree t ? t has b-node as the root

13
Unranked trees

• XML/HTML
• bib
• paper report book
  paper
• The number of children is not fixed by the label
• Two approaches
• 1. Generalize notion of tree automaton to
  unranked trees. L transition rules
• v(e) ? q , where e is regular expression over Q
• 2. Encode as ranked tree

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Encoding of unranked trees

• There are well-known methods of encoding to
  binary trees, we use
• encode(T) encodef(T)
• v if F1 F2 ?
• vright(encodef(F2)) if F1 ?, F2 ?
  ?
• encodef(v(F1), F2) vleft(encodef(F1))
  if F1 ? ?, F2 ?
• v(encodef(F1), encodef(F2))
  otherwise
• Where T v(F), v ? ? F ?
• F T, F
• Example
• becomes

15
k-testable tree languages

• Languages in which membership can be checked by
  just looking at subtrees of length k-1 that
  appear in the tree.
• k-roots
• k-forks
• k-subtrees

16
k-testable tree languages (cont.)

• An example t
• html
• head body
• title h1 table
• f2(t)
• html head body
• head body title h1 table

r2(t) html head
body s2(t) body head
table title h1 h1 table title
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k-testable algorithm Rico-Juan, et al.

• Given a set of positive examples T, a positive
  integer k
• Q, FS, ? Ø
• For each t ?T,
• Let R rk-1(t) F fk(t) S sk-1(t)
• Q Q ? R ? rk-1( F ) ? S
• FS FS ? R
• ? v(t1, ,tm) ? S ? ? ? ?m(v(t1, ,tm))
  v(t1, ,tm)
• ? v(t1, ,tm) ? F ? ? ? ?m(v(t1, ,tm))
  rk-1(v(t1, ,tm))

18
g-testable algorithm

• Idea generalize state transitions from forks
  that are not important for the extraction.
• Important forks are those that contain x and
  (possibly) the distinguishing context.

t
gen(t,1)
gen(t,2)
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g-testable algorithm

• Given a set of positive examples T, positive
  integer k and l
• FS, ?, Ss, CF, OF, OF Ø
• For each t ?T,
• Let R rk-1(t) F fk(t) S sk-1(t)
• Ss Ss ? S
• FS FS ? R
• ? v(t1, ,tm) ? S ? ? ? ?m(v(t1, ,tm))
  v(t1, ,tm)
• CF CF ? f f ? F, f contains x
• OF OF ? f f ? F, f does not contain x
• For each of ?OF,
• of gen(of,l)
• If of covers one of CF then
• OF OF ? of else OF OF ? of
• Let F OF ? CF
• Q Q ? F ? rk-1( F ) ? Ss
• ? v(t1, ,tm) ? F ? ? ? ?m(v(t1, ,tm))
  rk-1(v(t1, ,tm))

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g-testable algorithm example

• A set of examples T (with k 2 and l 1)
• html
  html
• head body head
  body
• title h1 x table title
  h1 x
• F f2(T)
• html head body
  body
• head body title h1 x h1 x
  table
• FS html
• OF html(head, body), head(title)
• CF body(h1, x), body(h1, x, table)
• OF html(, ), head()

• R r1(T) html
• Ss s1(T) table , title , h1, x

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g-testable algorithm example (cont.)

• F html(, ), head(), body(h1, x), body(h1,
  x, table)
• Transitions from the trees in the subtrees s1(T)
• ? (table) table
• ? (title) title
• ? (h1) h1
• ? (x) x
• Transitions from the trees in the generalized
  forks F
• ? (html(, )) html
• ? (head()) head
• ? (body(h1, x)) body
• ? (body(h1, x, table)) body
• Q html head body table title h1 x

22
Experiment

• Two benchmark datasets Internet Address Finder
  (IAF) and Quote Server (QS).
• Comparison with HMM, Stalker, and BWI.
• The highlights of our method
• More expressive.
• Doesnt require
• manual specifications of windows length of the
  prefix and suffix of the target field (HMM and
  BWI)
• special tokens of the delimiters such as gt
  (Stalker and BWI)
• embedded catalog tree (Stalker)
• The limitations
• The field that can be extracted limited to whole
  node
• Slower when extracting

23
Experiment results

• The results in are
• Dataset IAF-altname IAF-org
  QS-date QS-vol
  Shakespeare
• Prec Rec F1 Prec Rec
  F1 Prec Rec F1 Prec Rec F1
  Prec Rec F1
• --------------------------------------------------
  --------------------------------------------------
  ----------------------
• HMM 1.7 90 3.4 16.8 89.7
  28.4 36.3 100 53.3 18.4 96.2 30.9
• Stalker 100 - - 48.0 -
  - 0 - - 0
  - -
• BWI 90.9 43.5 58.8 77.5 45.9 57.7
  100 100 100 100 61.9 76.5
• k-testable 100 73.9 85 100 57.9 73.3
  100 60.5 75.4 100 73.6 84.8
  56.2 90 69.2
• g-testable 100 73.9 85 100 82.6 90.5
  100 60.5 75.4 100 73.6 84.8
  69.2 90 78.2
• Parameters 4 and (5,2) 2 and (3,2)
  2 and (3,2) 5 and (6,5)
  3 and (4,2)
• F1 is the harmonic mean of recall and precision
  
• The results of HMM, Stalker and BWI are adopted
  from Freitag Kushmerick

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Further work

• More generalization while using bigger context is
  achieved, but sometimes the binarisation makes
  the context far from the field of interest ? the
  generalization cannot go very far
• Work on the algorithm that can work directly with
  unranked trees.