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Implementing FastTBL in Oz

Leif Grönqvist (lgr@msi.vxu.se) & Fredrik Kronlid (kronlid@ling.gu.se). Implementing FastTBL in Oz. TBL. The training phase Input: Annotated corpus Rule templates Output: Sequence of rules (“best” rule first) Annotation phase Input: Sequence of rules Un-annotated corpus

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Implementing FastTBL in Oz

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  1. Leif Grönqvist (lgr@msi.vxu.se) & Fredrik Kronlid (kronlid@ling.gu.se) Implementing FastTBL in Oz

  2. TBL The training phase • Input: • Annotated corpus • Rule templates • Output: • Sequence of rules (“best” rule first) Annotation phase • Input: • Sequence of rules • Un-annotated corpus • Lexicon for initial annotations • Output • Annotated corpus

  3. Vanilla TBL (VTBL) • Rules are selected greedy • Corpus annotations updated after each rule selected • Continue until enough rules or no errors left • Number of possible rules in each iteration is very high • Grows by tagset, number of templates, and number of variables in templates • No results used from earlier iterations

  4. TBL à la Ramshaw & Marcus • Only applicable rules, correcting at least one sample is generated • The set of rules are saved between iterations • For each rule there is: • a score • a list of affected positions in the corpus • For all samples there is a list of applicable rules • Much faster than vanilla TBL • Needs much more memory than VTBL • The update of the needed structures takes a large amount of the time used during training

  5. FastTBL • Ngai, G. & Florian, R. (2001), Transformation-Based Learning in the Fast Lane, in ‘Proceedings of the 39th ACL Conference’. • Similar to Ramshaw & Marcus: • Only applicable rules, correcting at least one sample is generated • The set of rules are saved between iterations • For each rule there is: • a score consisting of the “Good” and “Bad” part • For each selected rule, the scores for all rules are updated • The “vicinity” of a sample tells the system which samples the classification may depend on • Much faster than Ramshaw & Marcus • Needs much less memory than R & M

  6. The algorithm in a nutshell

  7. Ngai & Florian’s description • Unclear what to store and how • When should chosen rules be applied to the corpus? • Notations like b(s) and p(b(s)) seem a bit sloppy – what do they mean? • b: rule, p: predicate, s: sample • How do we define vicinity? • An algorithm description should describe the algorithm

  8. Implementation • Oz • Data structures • Programming paradigms

  9. Oz • A multi-paradigm language • Object-oriented • Functional • Concurrent • Distributed • Declarative • Stateful • ....

  10. Data & Paradigm • We use functional programming, logic programming and imperative programming techniques. • Functional programming – some higher order functions • Extensive use of tuples – logic programming • Corpus – Array, Rule collection – Dictionary: stateful structures, assignment works

  11. Corpus representation • Many ways to access data in Oz • Data from μ-TBL (SUC) • Solution: • (Script to transform SUC to μ-TBL format) • Perl script for transforming SUC data into a tuple in a functor (Oz module) • Tuple converted to Array for mutability

  12. Template representation μ-TBL template representation allows • Constraints on any feature • Conjunction of constraints • Disjunction of positions • Target can change any feature tag:A>B <- wd:C@[0] & tag:D@[-1,-2] Requires an elaborate template compiler

  13. Templates cont’d FoztTBL templates allow • Constraints on tags and words • Conjunction of constraints • The only target is ”change tag A into B” • Our template format also give the possibility to generate rules on the form “change any tag to B”

  14. Templates cont’d We use an extremely simple template formalism: template(wd(0 0 c 0 0) tag(0 0 a d 0)) tag:A>B <- wd:C@[0] & tag:A@[0] & tag:D@[1] A FoztTBL template is instantiated into a Predicate by pattern matching

  15. Representing the rules • Rule: predicate and target + additional info • Tuple of tuples • rule(P T F UsedFlag) • P = predicate( wd(EMPTY EMPTY om EMPTY EMPTY) tag(EMPTY EMPTY pp EMPTY EMPTY) ) • T = target(sn) • F = f(Good, Bad) • UsedFlag – a flag to indicate whether the rule has been applied or not (tag:pp>sn <- wd:om@[0] & tag:pp@[0])

  16. Representing the rule collection • FastTBL requirement: instant (constant time) access to all rules with a certain predicate • Oz Dictionaries – hash tables with atoms as keys • Solution: • Dictionary • functions for rapid conversion predicate-atom

  17. Conclusions • FastTBL has: • Better time complexity than VTBL and RMTBL • Uses less memory than VTBL • Gives the same result • The multiparadigmatic nature of Oz makes programming easier • An algorithm appearing in a reviewed paper isn't necessarily complete, comprehensible or easily implementable.

  18. Future Work • Understand the algorithm in detail • Read the C++ code • Use the ideas in Ngai/Florian and reinvent it • Implement and read Ngai/Florian iteratively using a debugger • Finish off the implementation • Improve on the clarity of the implementation and the algorithm description • Make the template formalism more flexible, keeping its simplicity

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