Transcript Deep Processing for Restricted Domain QA - uni

Deep Processing for
Restricted Domain QA
Yi Zhang
Universität des Saarlandes
[email protected]
Why Deep?
Is Shallow Processing Enough?
 For TREC-like QA evaluation
 (in most cases) YES
 However, for restricted domain QA
 More complicated questions
 Less information redundancy for data
intensive approach
 Domain knowledge available
Deep Processing Provides
 More fine-grained linguistic analysis
 Long distance dependency
 Agreements
 …
 Semantic Representation
 MRS/RMRS
General Problems with Deep Processing
 Robustness
 Lexicon
 Compound NP
 Specificity
 “John saw Mary”
 Efficiency (not discussed here)
Deep Processing
 MRS/RMRS
 (Robust) Semantic representation with
underspecification.
 HPSG Grammars
 LinGO ERG Grammar
 Other grammars (German, Japanese, Modern
Greek, Norwegian, Chinese, …)
 HoG
 Hybrid shallow & deep processing architecture
with uniformed semantic representation (RMRS).
QA in QUETAL (1)
 Hybrid shallow & deep approach
 Cross-lingual QA
 QA on
 Texts
 Semi-structured documents
 Database
QA in QUETAL (2)
NLQ
Syntax Ana.
•Dependency Parser
•TAG for En/De Q.
Seman Ana.
•Seman Q. Ana.
•Q-type
•A-type
•Q-focus
IR Schema
GetData
Ans. Planning
& Generation
IR Query Planner
Result Merge
Info Source
Texts
IE
Fact DB
QA in QUETAL (3)
Deep processing in QUETAL
 HPSG grammar used for question
analysis.
 Documents are processed with relatively
shallow methods.
 Answer matching with RMRS.
Restricted Domain QA
 More complicated questions
 Less documents with better quality
 Domain specific ontology available
Restricted Domain QA – an Example
Where is the City Hall of Shanghai?
Shanghai City Planning Exhibition Hall[LOC_1] is
located to the east of the City Hall [LOC_2], …,
setting off with the crystal-like Grand
Theatre[LOC_3] to the west.
Between Shanghai City Planning
Exhibition Hall and the Grand
Theatre.
Domain Onto.
Open Topics
 Grammar extension & automated
lexicon acquisition
 Robust deep processing
 Semantic answer matching
 Cross-lingual
Grammar Extension
Tourism Domain
 ERG extended for
 “RONDANE” -- Norway mountain area tourism
 1.4K sentences
 15 word/sentence
 coverage > 74%
 Shanghai tourist guide from
http://www.shanghai.gov.cn
 1,600 sentences
 18 word/sentence
Test on RONDANE corpus
Test on RONDANE Corpus
Grammar Extension
 ERG lexicon
Lexicon
Entry #
Top 10 Leaf Types
Lexicon Coverage
Verb
2891
77%
Noun
6873
96%
Adj.
2505
90%
 It is relatively easier to automated the
lexicon acquisition for nouns
Automated Lexicon Acquisition
 POS tagging
 Name entity recognition
 Statistical models finding the best
lexical type for unknown noun.
Robust Deep Processing
 Back-off to RMRS generated with
intermediate or shallow parsers (HoG
architecture).
 Keep non-full parsing charts and
corresponding MRS fragments for
semantic answer matching.
Parse Disambiguation
 Select the best parse with statistical models
(Toutanova et al. 2002)
Answer Matching with (R)MRS
 Semantic answer matching
 Create semantic patterns for each
question type.
 where -> locate_v(e, x1, x2)
 Semantic distance measurement.
 pred1(x)&pred2(x) <-> pred1(x)&pred2(y)
 Query expansion
 Synonym substitution
 Semantic structure replacement
 give_v(e1, x1, x2, x3) => receive_v(e2, x2, x1, x3)
Work Plan
 Narrow down my focus onto one of
the topics above.
 Continue the Chinese HPSG grammar
development.
References
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Baldwin, Timothy, Emily M. Bender, Dan Flickinger, Ara Kim and Stephan Oepen (to appear) Road-testing the
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Ulrich Callmeier. 2002. PET – a platform for experimentation with efficient HPSG processing techniques. In
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Hans Uszkoreit. 2002. New chances for deep linguistic processing. In Proc. of the 19th International Conference
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Ann Copestake, Dan Flickinger, Ivan A. Sag, and Carl Pollard. 2003. Minimal recursion semantics: An introduction.
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Toutanova, Kristina, Christopher D. Manning, Stuart M. Shieber, Dan Flickinger, and Stephan Oepen. 2002. Parse
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Sozopol, Bulgaria.