1. Construct State Modification in the Arabic Treebank
Ryan Gabbard and Seth Kulick
University of Pennsylvania
ACL 6/18/08

2. Construct State (iDAfa إضافة) in Arabic
Outline
Construct State (iDAfa إضافة) in Arabic
What it is
The problem of attachment within an iDAfa
A Machine Learning Approach
Definition, Features, Results
Conclusion and Future Work
ACL 6/18/08

3. Construct State (iDAfa)
2+ words grouped tightly together
Like English compound or possessive
NOUN with NP complement (recursive)
(NP $awAriE streets (NP madiyn+ap city (NP luwnog byt$ Long Beach)))
شوارع هدينة لونغ بيتش
ACL 6/18/08

4. Construct State (iDAfa)
(NP $awAriE streets (NP (NP madiyn+ap city (NP luwnog byt$ Long Beach)) (PP fiy in (NP wilAy+ap state (NP kAliyfuwrniyA)))))
شوارع هدينة لونغ بيتش في ولاية كاليفورنيا
(Multiple) Modification at any level
Modifiers stacked up at end
No clear pattern of attachment level
ACL 6/18/08

5. Restriction on PP attachment in PTB
Multiple PP modifiers at same level
Allowed Not Allowed (NP (NP …) (NP (NP (NP …) (PP …) (PP …)) (PP …) (PP …))
Parser can learn that PPs attach to “base” (non-recursive) NPs (Collins, 99)
Not true for ATB, because of the iDAfa.
ACL 6/18/08

6. Modification of non-base NPs
(NP $awAriE streets (NP (NP madiyn+ap city (NP luwnog byt$ Long Beach)) (PP fiy in (NP wilAy+ap state (NP kAliyfuwrniyA)))))
(NP (NP streets) (PP of (NP (NP the city) (PP of (NP Long Beach)) (PP in (NP (NP the state) (PP of California)))))
ACL 6/18/08

7. Problem Summary and Approach
PP, ADJP attachment harder in ATB
Cannot rely on base NP constraint
PP attachment to a non-base NP nearly non-existent in PTB
16th most frequent dependency in ATB
PP attachment worse for ATB (Kulick,Gabbard,Marcus, 2006)
Treat attachment within iDAfa as problem independent of parser
ACL 6/18/08

8. The Task as a Machine Learning Problem
Definition
Instances are attachments Extract idafas and modifiers from corpus
Labels are level to attach at
Constraint: No attachments crossing levels
Technique
MaxEnt model to label attachments
Dynamic programming to enforce constraint
ACL 6/18/08

9. Machine Learning Features
Baseline: Only level of attachment
Non-Baseline Features
AttSym – POS tag or nonterminal label of modifier
Lex – (noun being modifed, head word of modifier)
TotDepth – (baseline ^ total depth of idafa ^ AttSym)
Simple GenAgr - (AttSym ^ gender suffixes of the words corresponding to lex)
Full GenAgr – Simple GenAgr also with number suffixes
ACL 6/18/08

10. Machine Learning Results
Features
Accuracy
Base
39.7
Base+AttSym
76.1
Base+Lex
58.4
Base+Lex+AttSym
79.9
Base+Lex+AttSym+TotDepth
78.7
Base+Lex+AttSym+GenAgr
79.3
ACL 6/18/08

11. For ML problem in this talk
Future Work
For ML problem in this talk
More feature investigation
Improved analysis of subclasses of iDAfas.
In context of real system
Analysis of iDAfa and attachment accuracy in current parsing
Get attachment problem out of parser Use current work as module after parsing
ACL 6/18/08