Machine Learning for NLP: Supervised learning techniques
نویسنده
چکیده
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منابع مشابه
Semisupervised Learning for Computational Linguistics
Semi-supervised learning is by no means an unfamiliar concept to natural language processing researchers. Labeled data has been used to improve unsupervised parameter estimation procedures such as the EM algorithm and its variants since the beginning of the statistical revolution in NLP (e.g., Pereira and Schabes (1992)). Unlabeled data has also been used to improve supervised learning procedur...
متن کاملSentiment Summerization and Analysis of Sindhi Text
Text corpus is important for assessment of language features and variation analysis. Machine learning techniques identify the language terms, features, text structures and sentiment from linguistic corpus. Sindhi language is one of the oldest languages of the world having proper script and complete grammar. Sindhi is remained less resourced language computationally even in this digital era. Vie...
متن کاملMachine Learning for NLP: Unsupervised learning techniques
• So far we have seen supervised learning (of classification): – learning based on a training set where labelling of instances represents the target (categorisation) function – classifier implements an approximation of the target funtion – outcome: a classification decision • Unsupervised learning: – learning based on unannotated instances; – outcome: a grouping of objects (instances and groups...
متن کاملMachine learning based Visual Evoked Potential (VEP) Signals Recognition
Introduction: Visual evoked potentials contain certain diagnostic information which have proved to be of importance in the visual systems functional integrity. Due to substantial decrease of amplitude in extra macular stimulation in commonly used pattern VEPs, differentiating normal and abnormal signals can prove to be quite an obstacle. Due to developments of use of machine l...
متن کاملBook Reviews: Semisupervised Learning for Computational Linguistics by Steven Abney
Semi-supervised learning is by no means an unfamiliar concept to natural language processing researchers. Labeled data has been used to improve unsupervised parameter estimation procedures such as the EM algorithm and its variants since the beginning of the statistical revolution in NLP (e.g., Pereira and Schabes 1992). Unlabeled data has also been used to improve supervised learning procedures...
متن کامل