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Information Extraction with Tree Automata Induction

Information Extraction with Tree Automata Induction. Raymond Kosala 1 , Jan Van den Bussche 2 , Maurice Bruynooghe 1 , Hendrik Blockeel 1 1 Katholieke Universiteit Leuven, Belgium 2 University of Limburg, Belgium. Outline. Introduction: information extraction (IE)

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Information Extraction with Tree Automata Induction

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  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. Extract certain fields of interest from a text Learner is trained with (positive) examples Each learner focuses on a single field marked with ‘x’ IE from unstructured documents 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. a x b b a a a a c c c c c c a b b x a c c c c c 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. < > ….. ………. …… . < > Tree automata learner Parsed and annotated Structured document Transformed Learning process

  8. < > ….. ………. …… . < > With each text nodes replaced, run the tree automaton Output Structured document Parsed Transformed 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. a c a b b c c c c 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 , qiand q  Q

  12. a a c b c c c a b c c a a c c a c c c 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

  14. a_left b_right a c b c d a d a 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

  17. 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. a a a b c b c * * * * d e * * 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):

  19. 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))}

  20. 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

  21. 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. • Doesn’t 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 “:” “>” (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]

  24. 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.

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