Word Set Probability Boosting : The AB / TAB Algorithms

Improved spontaneous dialogue recognition using dialogue and utterance triggers by adaptive probability boosting

Based on the observation that the unpredictable nature of conversational speech makes it almost impossible to reliably model sequential word constraints, the notion of word set error criteria is proposed for improved recognition of spontaneous dialogues. The basic idea in the TAB algorithm is to predict a set of words based on some a priori information, and perform a re-scoring pass wherein the...

Word Set Probability Boosting for Improved

Boosting Applied toe Word Sense Disambiguation

In this paper Schapire and Singer’s AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar–based approaches, which represent state–of–the–art accuracy on supervised WSD. In order to make boosting practical for a real learning domain o...

Boosting Applied to Word Sense Disambiguation

In this paper Schapire and Singer s AdaBoost MH boosting algorithm is applied to the Word Sense Disambiguation WSD problem Initial experiments on a set of selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar based ap proaches which represent state of the art accuracy on supervised WSD In order to make boosting practical for a real learning domain of thou ...

Using boosting and POS word graph tagging to improve speech recognition

The word graphs produced by a large vocabulary speech recognition system usually contain a path labelled with the correct utterance, but this is not always the highest scoring path. Boosting increases the probability of words which occur often in the word graph, which are in some sense robust. Adding syntactic information allows rescoring of arc probabilities with the possibility that more gram...