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Dependency Parsing

Dependency Parsing. Joakim Nivre. Dependency Grammar. Old tradition in descriptive grammar Modern theroretical developments: Structural syntax (Tesnière) Meaning-Text Theory (Mel’ čuk) Word Grammar (Hudson) Functional Generative Description (Prague) Basic idea:

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Dependency Parsing

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  1. Dependency Parsing Joakim Nivre

  2. Dependency Grammar • Old tradition in descriptive grammar • Modern theroretical developments: • Structural syntax (Tesnière) • Meaning-Text Theory (Mel’čuk) • Word Grammar (Hudson) • Functional Generative Description (Prague) • Basic idea: • Syntactic structure consists of binary, asymmetrical relations between the words of a sentence

  3. OBJ ADV PR SUB ATT VBmålade (painted) PNhan (he) JJdjärva (bold) NNtavlor (pictures) PPPå (In) NN60-talet (the-60’s) Dependency Representations • Directed graphs: • V is a set of nodes (tokens) • E is a set of arcs (dependency relations) • L is a labeling function on E (dependency types) • Example:

  4. Graph Constraints • Commonly imposed constraints: • Single-head (at most one head per node) • Connectedness (no dangling nodes) • Acyclicity (no cycles in the graph) • Projectivity: • An arc ij is projective iff, for every k occurring between i and j in the input string, ij. • A graph is projective iff every arc in A is projective.

  5. Dependency Parsing • Dependency-based grammar parsing: • Given a dependency grammar G and an input string x *, derive some or all of the dependency graphs y assigned to x by G. • Dependency-based text parsing: • Given a text T = (x1, …, xn), derive the correct dependency graph yi for every sentence xi T. • Text parsing may be grammar-driven or not.

  6. Parsing Methods • Three main approaches: • Dynamic programming algorithms applied to context-free (projective) dependency grammars • Eliminative parsing techniques applied to constraint-based formulations of (non-projective) dependency grammar • Deterministic parsing algorithms combined with weak grammars or data-driven methods

  7. Dynamic Programming • Early formalizations: • Hays/Gaifman: Equivalent to a subset of context-free grammars (roughly lexicalized) • Tabular parsing techniques (cf. CKY parsing) • Modern developments: • Link grammar (Sleator and Temperley) • Bilexical grammar (Eisner): • Lexicalized parsing in O(n3) time by combining spans instead of constituents.

  8. Constraint Satisfaction • Constraints on dependency graphs (Maruyama): pos(i)= D  [dep(i) = DET  pos(head(i)) = N  i < head(i)] pos(i)= N  [dep(i) = SUBJ  pos(head(i)) = V  i < head(i)] pos(i)= V  [dep(i) = ROOT  head(i) = nil] [head(i) = head(j)  dep(i) = dep(j)]  i = j • Graph satisfying the above constraints: DET SUBJ Vruns Da Ndog

  9. Parsing with Constraints • Eliminative parsing: • Start with all formally possible analyses (in a compact representation) • Eliminate representations that violate constraints until only valid analyses remain • Variations: • Weighted constraints • Transformational parsing

  10. Deterministic Parsing • Covington’s fundamental algorithm: • Accept words one by one starting at the beginning of the sentence, and try linking each word as head or dependent of every previous word. • Variations on shift-reduce parsing: • Standard (Kudo, Matsumoto, Yamada) • Arc-eager (Nivre)

  11. Arc-Eager Parsing • Configuration: C = S, I, A S = Stack I = Input (remaining) A = Arc relation (current) • Initialization: nil, W,  • Termination: S, nil, A for any S, A • Acceptance: S, nil, A if (W, A) is connected

  12. Transitions • Left-Arc (LA): wi|S, wj|I, A  S, wj|I, A  {(wj, wi)}if a : a  A dep(a) = wi • Right-Arc (RA): wi|S, wj|I, A  wj|wi|S, I, A  {(wi, wj)}if a : a  A  dep(a) = wj • Reduce (RE): wi|S, I, A  S, I, Aif a : a  A  dep(a) = wi • Shift (SH): S, wi|I, A  wi|S, I, A

  13. MaltParser • Robust, data-driven dependency parsing using a combination of: • Deterministic parsing (e.g. arc-eager) • Discriminative machine learning (e.g. MBL) • User-defined feature models: • Lexical features • Part-of-speech features • Dependency type features

  14. Why (Not) Dependency Parsing? • Potential advantages of dependency parsing: • Dependency relations are close to semantic relations, which facilitates semantic interpretation • Dependency representations are more constrained (less complex), which facilitates parsing • Dependency representations are more suitable for languages with free or flexible word order • Potential disadvantages: • Dependency representations are less expressive • Dependency representations are less well understood formally and computationally

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