UNSUPERVISED LEARNING USING GLOBAL FEATURES, INCLUDING FOR LOG-LINEAR MODEL WORD SEGMENTATION

UNSUPERVISED LEARNING USING GLOBAL FEATURES, INCLUDING FOR LOG-LINEAR MODEL WORD SEGMENTATION

Described is a technology for performing unsupervised learning using global features extracted from unlabeled examples. The unsupervised learning process may be used to train a log-linear model, such as for use in morphological segmentation of words. For example, segmentations of the examples are sa...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: TOUTANOVA KRISTINA N, POON HOIFUNG, CHERRY COLIN ANDREW
Format: Patent
Sprache:eng
Schlagworte:
ACOUSTICS
MUSICAL INSTRUMENTS
PHYSICS
SPEECH ANALYSIS OR SYNTHESIS
SPEECH OR AUDIO CODING OR DECODING
SPEECH OR VOICE PROCESSING
SPEECH RECOGNITION
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Beschreibung
Zusammenfassung:Described is a technology for performing unsupervised learning using global features extracted from unlabeled examples. The unsupervised learning process may be used to train a log-linear model, such as for use in morphological segmentation of words. For example, segmentations of the examples are sampled based upon the global features to produce a segmented corpus and log-linear model, which are then iteratively reprocessed to produce a final segmented corpus and a log-linear model.