1 / 33

Learning with Probabilistic Features for Improved Pipeline Models

Learning with Probabilistic Features for Improved Pipeline Models. Razvan C. Bunescu. Electrical Engineering and Computer Science Ohio University Athens, OH. bunescu@ohio.edu. EMNLP, October 2008. Introduction. NLP systems often depend on the output of other NLP systems. POS Tagging.

aurorette
Download Presentation

Learning with Probabilistic Features for Improved Pipeline Models

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. Learning with Probabilistic Featuresfor Improved Pipeline Models Razvan C. Bunescu Electrical Engineering and Computer Science Ohio University Athens, OH bunescu@ohio.edu EMNLP, October 2008

  2. Introduction • NLP systems often depend on the output of other NLP systems. POS Tagging Syntactic Parsing Question Answering Named Entity Recognition Semantic Role Labeling

  3. Traditional Pipeline Model: M1 • The best annotationfrom one stage is used in subsequent stages. x POS Tagging Syntactic Parsing • Problem: Errors propagate between pipeline stages!

  4. Probabilistic Pipeline Model: M2 • All possible annotationsfrom one stage are used in subsequent stages. x POS Tagging Syntactic Parsing probabilistic features • Problem: Z(x) has exponential cardinality!

  5. Probabilistic Pipeline Model: M2 • Feature-wise formulation: • When original i‘s are count features, it can be shown that:

  6. Probabilistic Pipeline Model • Feature-wise formulation: • When original i‘s are count features, it can be shown that: An instance of feature i , i.e. the actual evidence used from example (x,y,z).

  7. Probabilistic Pipeline Model • Feature-wise formulation: • When original i‘s are count features, it can be shown that: The set of all instances of feature i in (x,y,z), across all annotations zZ(x).

  8. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 RB VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  9. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 0.01 RB RB VBD VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  10. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 0.01 0.1 RB RB RB VBD VBD VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  11. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 0.01 0.1 0.001 RB RB RB VBD VBD VBD RB VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  12. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 0.01 0.1 0.001 0.001 RB RB RB VBD VBD VBD RB RB VBD VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  13. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 0.01 0.1 0.001 0.001 0.002 RB RB RB VBD VBD VBD RB RB RB VBD VBD VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  14. Example: POS Dependency Parsing • Feature i RB  VBD • The set of feature instances Fi is: 0.91 0.01 0.1 0.001 0.001 … … 0.002 RB RB RB VBD VBD VBD RB RB RB VBD VBD VBD The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11  N(N-1) feature instances in Fi .

  15. Example: POS Dependency Parsing • Feature i RB  VBD uses a limited amount of evidence: •  the set of feature instances Fi has cardinality N(N-1). •  computing takes O(N|P|2) time using a constrained version of • the forward-backward algorithm: • Therefore, computing i takes O(N3|P|2) time.

  16. Probabilistic Pipeline Model: M2 • All possible annotations from one stage are used in subsequent stages. x POS Tagging Syntactic Parsing polynomial time • In general, the time complexity of computing i depends on the complexity of the evidence used by feature i.

  17. Probabilistic Pipeline Model: M3 • The best annotationfrom one stage is used in subsequent stages, together with its probabilistic confidence: x POS Tagging Syntactic Parsing

  18. Probabilistic Pipeline Model: M3 • The best annotationfrom one stage is used in subsequent stages, together with its probabilistic confidence: x POS Tagging Syntactic Parsing The set of instances of feature i using only the best annotation

  19. Probabilistic Pipeline Model: M3 • Like the traditional pipeline model M1, except that it uses the probabilistic confidence values associated with annotation features. • More efficient than M2, but less accurate. • Example: POS  Dependency Parsing • shows features generated by template ti  tjand their probabilities. y: 0.81 0.92 0.85 0.97 0.95 0.98 0.91 0.97 0.90 0.98 DT1 NNS2 RB3 VBD4 EX5 MD6 VB7 NNS8 IN9 DT10 NN11 x: The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11

  20. Probabilistic Pipeline Models Model M2 Model M3

  21. Two Applications • Dependency Parsing • Named Entity Recognition x POS Tagging Syntactic Parsing x POS Tagging Syntactic Parsing Named Entity Recognition

  22. 1) Dependency Parsing • Use MSTParser [McDonald et al. 2005]: • The score of a dependency tree  the sum of the edge scores: • Feature templates use words and POS tags at positions u and v and their neighbors u  1 and v  1. • Use CRF [Lafferty et al. 2001] POS tagger: • Compute probabilistic features using a constrained forward-backwardprocedure. • Example: feature titj has probability p(ti, tj) • constrain the state transitions to pass through tags tiand tj.

  23. 1) Dependency Parsing • Two approximations of model M2: • Model M2’: • Consider POS tags independent: • p(ti RB,tj VBD|x) p(ti RB|x)  p(tj  VBD|x) • Ignore tags with low marginal probability: • p(ti)  1/(|P|) • Model M2”: • Like M2’, but use constrained forward-backward to compute marginal probabilities when the tag chunks are less than 4 tokens apart.

  24. 1) Dependency Parsing: Results • Train MSTParser on sections 2-21 of Pen WSJ Treebank using gold POS tagging. • Test MST Parser on section 23, using POS tags from CRF tagger. • Absolute error reduction of “only” 0.19% : • But POS tagger has a very high accuracy of 96.25%. • Expect more substantial improvement when upstream stages in the pipeline are less accurate.

  25. 2) Named Entity Recognition • Model NER as a sequence tagging problem using CRFs: z2: z1: DT1 NNS2 RB3 VBD4 EX5 MD6 VB7 NNS8 IN9 DT10 NN11 y: x: O I O O O O O O O O O The1 sailors2 mistakenly3 thought4 there5 must6 be7 diamonds8 in9 the10 soil11 • Flat features:unigram, bigram and trigram that extend either left or right: • sailors, the sailors, sailors RB, sailors RB thought… • Tree features: unigram, bigram and trigram that extend in any direction in the undirected dependency tree: • sailors thought, sailors  thought  RB, NNS thought  RB, …

  26. Named Entity Recognition: Model M2 x POS Tagging Syntactic Parsing Named Entity Recognition • Probabilistic features: • Example feature NNS2thought4  RB3:

  27. Named Entity Recognition: Model M3’ • M3’ is an approximation of M3 in which confidence scores are computed as follows: • Consider POS tagging and dependency parsing independent. • Consider POS tags independent. • Consider dependency arcs independent. • Example feature NNS2thought4  RB3: • Need to compute marginals p(uv|x).

  28. Probabilistic Dependency Features • To compute probabilistic POS features, we used a constrained version of the forward-backward algorithm. • To compute probabilistic dependency features, we use a constrained version of Eisner’s algorithm: • Compute normalized scores n(uv | x) using the softmax function: • Transform scores n(uv|x) into probabilities p(uv|x) using isotonic regression [Zadrozny & Elkan, 2002].

  29. Named Entity Recognition: Results • Implemented the CRF models in MALLET [McCallum, 2002] • Trained and tested on the standard split from the ACE 2002 + 2003 corpus (674 training, 97 testing). • POS tagger and MSTParser were trained on sections 2-21 of WSJ Treebank • Isotonic regression for MSTParser on section 23. Area under PR curve

  30. Named Entity Recognition: Results • M3’ (probabilistic) vs. M1 (traditional) using tree features:

  31. Conclusions & Related Work • A general method for improving the communication between consecutive stages in pipeline models: • based on computing expectations for count features. • an efective method for associating probabilities with output substructures. • adds polynomial time complexity to pipeline whenever the inference step at each stage is done in polynomial time. • Can be seen as complementary to the sampling approach of [Finkel et al. 2006]: • approximate vs. exact in polynomial time. • used in testing vs. used in training and testing.

  32. Future Work • Try full model M2 / its approximation M2’ on NER. • Extend model to pipeline graphs containing cycles.

  33. Questions?

More Related