Foundations of statistical natural language processing
Foundations of statistical natural language processing
A DOP model for semantic interpretation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The structure of shared forests in ambiguous parsing
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
An all-subtrees approach to unsupervised parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning auxiliary fronting with grammatical inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A unified model of structural organization in language and music
Journal of Artificial Intelligence Research
Item-based constructions and the logical problem
PMHLA '05 Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition
Natural language grammar induction with a generative constituent-context model
Pattern Recognition
Darwinised data-oriented parsing: statistical NLP with added sex and death
CACLA '09 Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition
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Unsupervised Data-Oriented Parsing models (U-DOP) represent a class of structure bootstrapping models that have achieved some of the best unsupervised parsing results in the literature. While U-DOP was originally proposed as an engineering approach to language learning (Bod 2005, 2006a), it turns out that the model has a number of properties that may also be of linguistic and cognitive interest. In this paper we will focus on the original U-DOP model proposed in Bod (2005) which computes the most probable tree from among the shortest derivations of sentences. We will show that this U-DOP model can learn both rule-based and exemplar-based aspects of language, ranging from agreement and movement phenomena to discontiguous contructions, provided that productive units of arbitrary size are allowed. We argue that our results suggest a rapprochement between nativism and empiricism.