1. Parsing in Multiple Languages
Dan Bikel
University of Pennsylvania
August 22nd, 2003

2. Problems with many parser designs
Many parsing models are largely language-independent, but their implementations do not have layer of abstraction for language-specific information
Most generative parsers do not have easy method for experimenting with different parameterizations and back-off levels
Most parsers need large database of smoothed probability estimates, but this database is often tightly coupled with decoder
Parallel and/or distributed computing cannot easily be exploited

3. Parsing Architecture for Speed and Flexibility
Language
Language package
DecoderServer 1
ModelCollection
CKY Client 1
CKY Client 2 M M
DecoderServer N
ModelCollection
CKY Client N
Switchboard
Object server

4. Architecture for Parsing II
Highly parallel, multi-threaded
Can take advantage of, e.g., clustered computing environment
Fully fault-tolerant
Significant flexibility: layers of abstraction
Optimized for speed
Highly portable for new domains, including new languages

5. Layer of Abstraction: Language Package
Four, easy-to-build components
Treebank: encapsulates linguistic information, Treebank-specific information
isConjunction(tag), isConjunction(word)
isNP(nonterminal)
isSentence(nonterminal)
isVerb(preterminal subtree)
Training
Preprocessing, tree augmentations
Head finder
Reads in small, head rules file (35 lines for English)
Word features

6. Tree augmentation Parsers operate over augmented tree space, T+
Examples of tree augmentations
Head lexicalization: NP ® NP(thing,NN)
Argument identification: NP ® NP-A
Chiang & Bikel (2002) provided treep
New, portable syntax for augmenting tree nodes
Method for reestimating parser models in the augmented space such that P(S,T) is maximized

7. Rapid Portability to New Languages and New Data Sets
Bikel & Chiang (2000) described porting two parsing models developed for English to Chinese
BBN: LR 69.0, LP 74.8 (≤ 40 words)
Chiang: LR 76.8, LP 77.8 (≤ 40 words)
Original design goal for parsing engine: develop new language packages in 1–2 weeks
Developed Chinese language package for new engine in one and a half days
Results with new parsing engine using CTB (100k words)
LR 77.0, LP 81.6 (≤ 40 words, hand segmented)
Latest results with new engine using CTB 3.0 (250k words)
LR 78.8, LP 82.4 (≤ 40 words, hand segmented)

8. Tools in Use English parser Chinese parser
Gets state-of-the-art results
Training data: Penn Treebank, §§02-21, ~1 million words
Results: LR 89.90, LP on §00 (≤40 words)
Fast: trains in minutes, can use cluster to parse an entire WSJ section in about 5 minutes
Chinese parser
Dan Jurafsky and one of his grad students, Honglin Sun, at the University of Colorado (predicate-argument labeling for IE)
Kevin Knight and Philipp Koehn at ISI Research (MT)
In-house users
Dan Gildea (MT)
Yuan Ding (MT)
Chinese Treebank annotators (bootstrapping treebank annotation)

9. Tools in Use II Arabic Parser
Once representation/format issues ironed out, porting to Arabic also proceeded rapidly
Encouraging preliminary results
Training data: 150k words
LR 75.6, LP 77.4 (≤40 words, mapped gold-standard tags)
Currently being used by Rebecca Hwa at the University of Maryland (MT)
We believe
Much room for improvement after analysis of initial results, but…
performance is already more than good enough for bootstrapping Treebank annotation

10. Tools in Use III Classical Portugese Parser
Rapid development of language package (with help of Tony Kroch)
Successfully used for bootstrapping of treebank for the Statistical Physics, Pattern Recognition and Language Change project at State University of Campinas, São Paolo, Brazil

11. Obtaining Tools and Data
For my parsing engine (English, Arabic, Chinese and soon, Korean), please contact me
For Penn’s Chinese segmenter, contact Bert Xue
Data
Treebanks and PropBanks

12. Future Work
Develop Java version of David Chiang’s and my tree augmentation program, treep, and incorporate into parsing engine
Develop user-level documentation for developing a new language package (in progress)
Provide layer of abstraction for language-specific lexical resources, such as semantic hierarchy (in progress)
Explore language-independent parsing model changes, to find those that yield positive effect across all languages
Employ the layers of abstraction for exploring parameter space
Perform “regression tests” across treebanks/languages

13. fin