1. EMPOWER 2 Empirical Methods for Multilingual Processing, ‘Onoring Words, Enabling Rapid Ramp-up Martha Palmer, Aravind Joshi, Mitch Marcus, Mark Liberman, Tony Kroch, Lyle Ungar University of Pennsylvania March 23, 2000 TIDES KICKOFF

2. Penn approach Relies on lexically based linguistic analysis –Humans annotate naturally occurring text (hand correct output of automatic parsers, e.g. Fiddich, XTAG) –Train statistical POStaggers, parsers, etc. –Common thread is predicate-argument structure Hypothesis: More linguistically sophisticated analyzers More accurate output

3. EMPOWER 2 Approach Annotations enriched with semantics and pragmatics Provide companion lexicons for annotated corpora Extend our coverage to other languages Goal – Parallel annotated corpora/lexicons will enable rapid ramp-up of MT

4. TOOLS/RESOURCES Morphological Analyzers Stochastic parsers Lexicalized grammars Lexical classifications for cross-lingual mappings LANGUAGES English Chinese Korean Hindi/Tamil Faster development of Tools/annotation:

5. Current Status English Q&A using coreference English annotation –adding semantics to Penn TreeBank –creating companion lexicon Korean/English annotation –syntactic annotation and some semantics –companion transfer lexicon Chinese annotation –syntactic annotation (Chinese TreeBank)

6. English Q&A – Tom Morton TREC-8 Approach Extract sentences based on: –Words in the sentence –Category of the answer –Words in co-reference relationships pronouns common nouns dates Results –Placed 4th out of 20 participants.

7. Examples (pronouns) Who killed Lee Harvey Oswald? - demo –..., and the hat. There was the suit he wore on the day he(JACK RUBY) killed Oswald, a diamond-studded watch, a silver and diamond ring, two pairs of swim trunks, a shower cap, an athletic supporter and a letter written to a woman. Other than th … Future Plans –use WordNet and syntactic constructions to determine semantic categories of noun phrases –Cross-document co-reference

8. Semantic Annotation – Hoa Dang, Joseph Rosenzweig, John Duda Current syntactic annotation –POS, phrase structure bracketing –Logical Subject, locative, temporal adjuncts New semantic augmentations –Sense tag verbs and noun arguments/adjuncts –Predicate-argument relations for verbs, label arguments (arg0, arg1, arg2)

9. First Experiment (Siglex99) WSJ 5K word corpus –running text –WordNet 1.6 2100 words sense tagged twice (10 days) –89% inter-annotator agreement –700 verb tokens – 81% agreement (disagreement in 90/350 verb tokens) Automatic predicate-argument labeling –81% precision on 162 structures –Hand corrected 2100 words in one day

10. Example I was shaking the whole time. The walls shook; the building rocked. ;

11. Second Experiment: Methodology ( 150K target – Penn TreeBank II, with Christiane Fellbaum ) Sense tagging –Two human annotators (replace one with automatic WSD if possible) –WordNet senses, but allow for revision of entries Predicate argument labels –Rosenzweig’s converter –Uses TreeBank “cues” –Consults lexical semantic KB Verb subcategorization frames and alternations Ontology of noun-phrase referents Multi-word lexical items XML annotation in external file referencing IDs

12. Predicate-Argument Labeling: one raid tree – Rosenzweig’s converter

14. New language/English MT Components New language –Morphological Analyzer (POStags) –Parser/Generator –TreeBank –Companion pred-arg lexicon English –POStagger –Parser/Generator –TreeBank –Companion pred-arg lexicon Transfer Lexicon

15. Korean/English MT Chunghye Han, Juntae Yoon, Meesook Kim, Eonsuk Ko (CoGenTex/Penn/Systran: ARL) Parallel TreeBanks for Korean/English enable –Training of domain-specific Korean parsers Collins parser and SuperTagger (also English) –Alignment of Korean/English structures Attempt automatic and semi-automatic testing and generation of transfer lexicon (with CoGenTex) Apply statistical MT techniques Lexical semantics (Systran, mapped to EuroWordNet-IL) should improve –Accuracy of parsers – Recovery of dropped arguments http://www.cis.upenn.edu/~xtag/koreantag/index.html

16. Additional Korean/English parallel data? Current parallel corpus not public domain Can use tools trained on this corpus to quickly annotate additional corpora –Translate sections of Penn TreeBank into Korean? –Use existing Korean newswire text – translate into English? –Both?

17. Example translation

18. Transfer lexicon entries: Mapping predicate argument structures across languages

19. Chinese TreeBank – DOD Fei Xia, Ninwen Xue, Fu-dong Chiou http://www.ldc.upenn.edu/ctb/index.html http://www.ldc.upenn.edu/ctb/index.html Workshop of interested members of Chinese community, June ‘98 Guidelines and sample files posted on web –Segmentation, March, ‘99 –POStagging, March, ‘99 –Bracketing, First pass, October, ’99 –Bracketing, Second Pass, May, ’00 95%+ inter-annotator consistency Release of 100K annotated data, July, ’00 Follow-up workshop, Hong Kong, ACL’00

21. Goal for Chinese Parallel, annotated corpora – Hong Kong news? Parse English with WSJ trained parsers, correct Extend English TreeBank lexicon as needed Parse Chinese with CTB trained parsers, correct Start with lexicon extracted from CTB, extend Experiment with using semi-automated techniques wherever possible to speed up process

22. Past results XTAG project http://www.cis.upenn.edu/~xtag/ Penn TreeBank http://www.cis.upenn.edu/~treebank/ Enabled the development of tools: POStaggers, parsers, co-reference, etc http://www.ircs.upenn.edu/knowledge/licensing.html