An Efficient, Probabilistically Sound Algorithm for Segmentation andWord Discovery
Machine Learning - Special issue on natural language learning
An Algorithm for Segmenting Categorical Time Series into Meaningful Episodes
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A statistical model for word discovery in transcribed speech
Computational Linguistics
The Markov Expert for Finding Episodes in Time Series
DCC '05 Proceedings of the Data Compression Conference
Chinese text segmentation with MBDP-1: making the most of training corpora
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Efficient unsupervised recursive word segmentation using minimum description length
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Voting experts: An unsupervised algorithm for segmenting sequences
Intelligent Data Analysis
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
From phoneme to morpheme: another verification using a corpus
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Fully unsupervised word segmentation with BVE and MDL
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Word segmentation as general chunking
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Online Learning Mechanisms for Bayesian Models of Word Segmentation
Research on Language and Computation
A regularized compression method to unsupervised word segmentation
SIGMORPHON '12 Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology
| Hi-index | 0.00 |
BOOTSTRAP VOTING EXPERTS (BVE) is an extension to the VOTING EXPERTS algorithm for unsupervised chunking of sequences. BVE generates a series of segmentations, each of which incorporates knowledge gained from the previous segmentation. We show that this method of bootstrapping improves the performance of VOTING EXPERTS in a variety of unsupervised word segmentation scenarios, and generally improves both precision and recall of the algorithm. We also show that Minimum Description Length (MDL) can be used to choose nearly optimal parameters for VOTING EXPERTS in an unsupervised manner.