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Graph Based Dependency Parsing Wei Qiu December 15, 2011 . . - PowerPoint PPT Presentation

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. . . . . . Graph Based Dependency Parsing Graph Based Dependency Parsing Wei Qiu December 15, 2011 . . . . . . Graph Based Dependency Parsing Outline Outline 1 Introduction to dependency parsing 2 Graph based dependency

  1. . . . . . . Graph Based Dependency Parsing Graph Based Dependency Parsing Wei Qiu ฀ ฀ December 15, 2011

  2. . . . . . . Graph Based Dependency Parsing Outline Outline 1 Introduction to dependency parsing 2 Graph based dependency parsing 3 Parsing algorithms Projective dependency parsing Non-projective depency parsing(MST) 4 Learning framework 5 Evaluation

  3. . . . . . . Graph Based Dependency Parsing Introduction to dependency parsing Outline 1 Introduction to dependency parsing 2 Graph based dependency parsing 3 Parsing algorithms Projective dependency parsing Non-projective depency parsing(MST) 4 Learning framework 5 Evaluation

  4. . saw . xcomp . nsubj . poss . . yesterday . . Mary . . . John . . Output: a dependency tree John saw Mary yesterday. Input: a sentence What is dependency parsing? Introduction to dependency parsing Graph Based Dependency Parsing . . . . dobj

  5. . . . PRED . SBJ . OBJ TMP yesterday . . PU Directed tree with root The arcs indicate certain grammatical relatoins between words Properties Single-headness :Each word depends on exactly one parent. Connectivity . . . Dependency tree . . . . Graph Based Dependency Parsing Introduction to dependency parsing . Mary . Root . John . saw . acyclic

  6. . Mary The edges above the words don’t cross. PU . TMP . OBJ . SBJ . PRED . . yesterday . . . . saw . John . Root . . Projective dependency tree Introduction to dependency parsing Graph Based Dependency Parsing . . . . Word and its decendents form a substring.

  7. . Mary The edges above the words don’t cross. PU . TMP . OBJ . SBJ . PRED . . yesterday . . . . saw . John . Root . . Projective dependency tree Introduction to dependency parsing Graph Based Dependency Parsing . . . . Word and its decendents form a substring.

  8. . Mary The edges above the words don’t cross. PU . TMP . OBJ . SBJ . PRED . . yesterday . . . . saw . John . Root . . Projective dependency tree Introduction to dependency parsing Graph Based Dependency Parsing . . . . Word and its decendents form a substring.

  9. . Mary The edges above the words don’t cross. PU . TMP . OBJ . SBJ . PRED . . yesterday . . . . Projective dependency tree . . . . Graph Based Dependency Parsing Introduction to dependency parsing . saw . . Root . John . Word and its decendents form a substring.

  10. . a . who . . was . . . . . young . . lady . . . yesterday Mary . Non-projective dependency tree . . . . Graph Based Dependency Parsing Introduction to dependency parsing John saw Mary yesterday who was a young lady. . . . Root . John . saw .

  11. . a . who . . was . . . . . young . . lady . . . yesterday Mary . Non-projective dependency tree . . . . Graph Based Dependency Parsing Introduction to dependency parsing John saw Mary yesterday who was a young lady. . . . Root . John . saw .

  12. . a . who . . was . . . . . young . . lady . . . yesterday Mary . Non-projective dependency tree . . . . Graph Based Dependency Parsing Introduction to dependency parsing John saw Mary yesterday who was a young lady. . . . Root . John . saw .

  13. . a . who . . was . . . . . young . . lady . . . yesterday Mary . John saw Mary yesterday who was a young lady. . . . . Graph Based Dependency Parsing Introduction to dependency parsing Non-projective dependency tree . . . . Root . John . saw .

  14. . . . . . . Graph Based Dependency Parsing Introduction to dependency parsing Data-driven parsing framework

  15. . . . . . . Graph Based Dependency Parsing Graph based dependency parsing Outline 1 Introduction to dependency parsing 2 Graph based dependency parsing 3 Parsing algorithms Projective dependency parsing Non-projective depency parsing(MST) 4 Learning framework 5 Evaluation

  16. . . . . . . Graph Based Dependency Parsing Graph based dependency parsing What is graph based dependcy parsing? Graph-based models: Define a space of candidate dependency trees of input sentene. Learning: induce a model for scoring a candidate tree Parsing: find a tree with the highest score given the model

  17. . . PU . TMP . OBJ . SBJ . PRED . . yesterday . . . . Mary . . saw . . John . . . . . . PU . TMP . OBJ . SBJ . PRED . . yesterday . . . . Mary . . saw . . John . . Root Root . . John SBJ . PRED . . yesterday . . Mary . saw . . OBJ Root . . Candidate trees for “John saw mary yesterday” What is graph based dependency parsing? Graph based dependency parsing Graph Based Dependency Parsing . . . . . . . . . PU . TMP . OBJ . SBJ . PRED . yesterday . TMP . Mary . saw . John . Root . . PU . . . .

  18. . yesterday . . TMP . OBJ . SBJ . PRED . . . . . . Mary . . saw . . John . . Root . PU . . yesterday . PU . TMP . OBJ . SBJ . PRED . . . . . . Mary . . saw . . John . . Root . . . . . OBJ . SBJ . PRED . . yesterday . . Mary . saw John TMP . Root . . Candidate trees for “John saw mary yesterday” What is graph based dependency parsing? Graph based dependency parsing Graph Based Dependency Parsing . . . . . . PU . . . TMP . . OBJ . . SBJ . . PRED . PU . yesterday . . Mary . saw . John . Root . . . . .

  19. . . PRED . . SBJ . . OBJ . . TMP . . PU X : an input sentence Y : a candidate dependency tree arg max arg max . . . . . . . yesterday . Graph Based Dependency Parsing Graph based dependency parsing Arc-factored model . . . . John . saw . Root Mary x i → x j : a dependency edge from word i to word j Φ( X ) : the set of possible depenent trees over X Y ∗ = score ( Y | X ) Y ∈ Φ( X ) ∑ = score ( x i → x j ) Y ∈ Φ( X ) ( x i → x j ) ∈ Y

  20. . arg max . . . . Graph Based Dependency Parsing Graph based dependency parsing Arc-factored model arg max . Mcdonald2005: Y ∗ = score ( Y | X ) Y ∈ Φ( X ) ∑ = score ( x i → x j ) Y ∈ Φ( X ) ( x i → x j ) ∈ Y score ( x i → x j ) can be either probability or not. w · ⃗ score ( x i → x j ) = ⃗ f ( x i → x j )

  21. . Root . Mary . saw . John . . . Arc-factored model Graph based dependency parsing Graph Based Dependency Parsing . . . . yesterday

  22. . Root . Mary . saw . John . . . Arc-factored model Graph based dependency parsing Graph Based Dependency Parsing . . . . yesterday

  23. . . . . . . Graph Based Dependency Parsing Parsing algorithms Outline 1 Introduction to dependency parsing 2 Graph based dependency parsing 3 Parsing algorithms Projective dependency parsing Non-projective depency parsing(MST) 4 Learning framework 5 Evaluation

  24. . Outline 4 Learning framework Non-projective depency parsing(MST) Projective dependency parsing 3 Parsing algorithms 2 Graph based dependency parsing 1 Introduction to dependency parsing Projective dependency parsing . Parsing algorithms Graph Based Dependency Parsing . . . . 5 Evaluation

  25. . . saw . . . yesteday . . saw . . . Mary . saw . . . . saw . . John Ideas Legal subtree spans on contiguous string. Subtree can be built from smaller subtrees step by step.In each step, always . PU . TMP . . . . Graph Based Dependency Parsing Parsing algorithms Projective dependency parsing Naive CYK-like parsing . . . Root . John . saw . Mary . yesterday . . . PRED . SBJ . OBJ . subtrees!(Exactly CYK!) combine only 2

  26. . . . . . saw . . . John (John) (saw mary) . saw . . . . . Mary . . Mary . . . John . . saw . . Mary . . saw Mary . . . John . John . . . . . . John . . saw . . . John John saw mary saw Example Naive CYK-like parsing: example Projective dependency parsing Parsing algorithms Graph Based Dependency Parsing . . . . . . . Mary . Mary . . . . saw . . . . . . saw . John . . saw . . . .

  27. . . . . . saw . . . John (John) (saw mary) . saw . . . . . Mary . . Mary . . . John . . saw . . Mary . . saw Mary . . . John . John . . . . . . John . . saw . . . John John saw mary saw Example Naive CYK-like parsing: example Projective dependency parsing Parsing algorithms Graph Based Dependency Parsing . . . . . . . Mary . Mary . . . . saw . . . . . . saw . John . . saw . . . .

  28. . . . . . saw . . . John (John) (saw mary) . saw . . . . . Mary . . Mary . . . John . . saw . . Mary . . saw Mary . . . John . John . . . . . . John . . saw . . . John John saw mary saw Example Naive CYK-like parsing: example Projective dependency parsing Parsing algorithms Graph Based Dependency Parsing . . . . . . . Mary . Mary . . . . saw . . . . . . saw . John . . saw . . . .

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