Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Making large-scale support vector machine learning practical
Advances in kernel methods
The Constraint Language for Lambda Structures
Journal of Logic, Language and Information
A machine learning approach to modeling scope preferences
Computational Linguistics
Determining the Scope of English Quantifiers
Determining the Scope of English Quantifiers
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Handling scope ambiguities in English
ANLC '88 Proceedings of the second conference on Applied natural language processing
Quantifier scoping in the SRI core language engine
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
An algebra for semantic construction in constraint-based grammars
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Deep linguistic processing for spoken dialogue systems
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Boeing's NLP system and the challenges of semantic representation
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Quantifier scope disambiguation using extracted pragmatic knowledge: preliminary results
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
An efficient enumeration algorithm for canonical form underspecified semantic representations
FG'09 Proceedings of the 14th international conference on Formal grammar
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We present the first work on applying statistical techniques to unrestricted Quantifier Scope Disambiguation (QSD), where there is no restriction on the type or the number of quantifiers in the sentence. We formulate unrestricted QSD as learning to build a Directed Acyclic Graph (DAG) and define evaluation metrics based on the properties of DAGs. Previous work on statistical scope disambiguation is very limited, only considering sentences with two explicitly quantified noun phrases (NPs). In addition, they only handle a restricted list of quantifiers. In our system, all NPs, explicitly quantified or not (e.g. definites, bare singulars/plurals, etc.), are considered for possible scope interactions. We present early results on applying a simple model to a small corpus. The preliminary results are encouraging, and we hope will motivate further research in this area.