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Katalog Online Perpustakaan Universitas Ma Chung Villa Puncak Tidar N-01 Malang - Jawa Timur.
DDC v.22
Klasifikasi & Katalogisasi DDC versi 22
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| Title | An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery |
| Edition | Volume 34, Numbers 1-3 |
| Call Number | |
| ISBN/ISSN | 0885-6125 |
| Author(s) | BRENT, MICHAEL R. Cardie, Claire Mooney, Raymond |
| Subject(s) | |
| Classification | |
| Series Title | Machine Learning | GMD | Electronic Journal |
| Language | English |
| Publisher | Springer Netherlands |
| Publishing Year | 1999 |
| Publishing Place | Netherlands |
| Collation | 35p |
| Abstract/Notes | This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that generated the text. The fundamental structure of the model is specified abstractly so that the detailed component models of phonology, word-order, and word frequency can be replaced in a modular fashion. The model yields a language-independent, prior probability distribution on all possible sequences of all possible words over a given alphabet, based on the assumption that the inputwas generated by concatenatingwords from a fixed but unknown lexicon. The model is unusual in that it treats the generation of a complete corpus, regardless of length, as a single event in the probability space. Accordingly, the algorithm does not estimate a probability distribution on words; instead, it attempts to calculate the prior probabilities of various word sequences that could underlie the observed text. Experiments on phonemic transcripts of spontaneous speech by parents to young children suggest that our algorithm is more effective than other proposed algorithms, at least when utterance boundaries are given and the text includes a substantial number of short utterances. |
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