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

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