1 / 14

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:

jolene
Télécharger la présentation

Dependency Parsing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related