Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Mental Processes: Studies in Cognitive Science
Mental Processes: Studies in Cognitive Science
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
What is the minimal set of fragments that achieves maximal parse accuracy?
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Cognition of Basic Musical Structures
The Cognition of Basic Musical Structures
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Is there a general mechanism that governs the perception of phrase structure in music and language? While it is usually assumed that humans have separate faculties for music and language, this work focuses on the commonalities rather than on the differences between these modalities, aiming at finding a deeper "faculty". We present a series of data-oriented parsing (DOP) models which aim at balancing the simplest structure with the most likely structure of an input. Experiments with the Essen Folksong Collection and the Penn Treebank show that exactly the same model with the same parameter setting achieves maximum parse accuracy for both music and language. This suggests an interesting parallel between musical and linguistic processing. We show that our results outperform both the melodic component of Temperley (2001) and the musical parser of Bod (2001b).