Preliminary analysis of a breadth-first parsing algorithm: theoretical and experimental results
Natural language parsing systems
Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
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
Statistical Language Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Mental Processes: Studies in Cognitive Science
Mental Processes: Studies in Cognitive Science
Squibs and discussions: the DOP Estimation method is biased and inconsistent
Computational Linguistics
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Combining Grammar-Based and Memory-Based Models of Perception of Time Signature and Phase
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Data-Oriented Parsing
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
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
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
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Aspects of pattern-matching in Data-Oriented Parsing
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Computational complexity of probabilistic disambiguation by means of tree-grammars
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
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
An efficient implementation of a new DOP model
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
TWO GRAMMATICAL INFERENCE APPLICATIONS IN MUSIC PROCESSING
Applied Artificial Intelligence
Melody Track Selection Using Discriminative Language Model
IEICE - Transactions on Information and Systems
Information dynamics: patterns of expectation and surprise in the perception of music
Connection Science - Music, Brain, Cognition
Unsupervised parsing with U-DOP
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A linguistic investigation into unsupervised DOP
CACLA '07 Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition
A methodological contribution to music sequences analysis
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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Is there a general model that can predict the perceived phrase structure in language and music? While it is usually assumed that humans have separate faculties for language and music, this work focuses on the commonalities rather than on the differences between these modalities, aiming at finding a deeper "faculty". Our key idea is that the perceptual system strives for the simplest structure (the "simplicity principle"), but in doing so it is biased by the likelihood of previous structures (the "likelihood principle"). We present a series of data-oriented parsing (DOP) models that combine these two principles and that are tested on the Penn Treebank and the Essen Folksong Collection. Our experiments show that (1) a combination of the two principles outperforms the use of either of them, and (2) exactly the same model with the same parameter setting achieves maximum accuracy for both language and music. We argue that our results suggest an interesting parallel between linguistic and musical structuring.