| lr,8-6-N01-1003,ak | rules </term> automatically learned from <term> | training | data </term> . We show that the trained <term> | #1430 The SPR uses ranking rules automatically learned fromtraining data. | |
| </term> , including the influence of various | training | and testing factors on <term> predictive | #2189 We report on different aspects of the predictive performance of our models, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables. | ||
| lr,15-1-N03-1001,ak | <term> manual transcription </term> of <term> | training | data </term> . The method combines <term> domain | #2221 This paper describes a method for utterance classification that does not require manual transcription oftraining data. | |
| tech,4-3-N03-1001,ak | </term> . In our method , <term> unsupervised | training | </term> is first used to train a <term> phone | #2261 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. | |
| lr,2-1-N03-2003,ak | tagging result </term> . Sources of <term> | training | data </term> suitable for <term> language modeling | #3017 Sources oftraining data suitable for language modeling of conversational speech are limited. | |
| lr,7-2-N03-2003,ak | limited . In this paper , we show how <term> | training | data </term> can be supplemented with <term> | #3036 In this paper, we show howtraining data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. | |
| bootstrapping procedure </term> is implemented as | training | two <term> successive learners </term> . First | #3341 The bootstrapping procedure is implemented as training two successive learners. | ||
| tech,1-4-N03-2036,ak | trigram language model </term> . During <term> | training | </term> , the <term> blocks </term> are learned | #3446 Duringtraining, the blocks are learned from source interval projections using an underlying word alignment. | |
| lr,27-2-P03-1050,ak | parallel corpus </term> as its sole <term> | training | resources </term> . No <term> parallel text | #4475 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its soletraining resources. | |
| other,7-3-P03-1050,ak | parallel text </term> is needed after the <term> | training | phase </term> . <term> Monolingual , unannotated | #4485 No parallel text is needed after thetraining phase. | |
| lr,34-5-P03-1051,ak | expanded <term> vocabulary </term> and <term> | training | corpus </term> . The resulting <term> Arabic | #4742 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary andtraining corpus. | |
| lr,11-2-P03-1058,ak | automatically acquire <term> sense-tagged | training | data </term> from <term> English-Chinese parallel | #4835 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. | |
| lr,13-1-H05-1012,ak | Arabic-English </term> based on <term> supervised | training | data </term> . We demonstrate that it is | #5289 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data. | |
| lr,8-2-H05-1012,ak | demonstrate that it is feasible to create <term> | training | material </term> for problems in <term> machine | #5300 We demonstrate that it is feasible to createtraining material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance. | |
| model,11-3-H05-1064,ak | assignments based on a <term> discriminative | training | criterion </term> . <term> Training </term> and | #5482 The model learns to automatically make these assignments based on a discriminative training criterion. | |
| tech,0-4-H05-1064,ak | discriminative training criterion </term> . <term> | Training | </term> and <term> decoding </term> with the <term> | #5485 The model learns to automatically make these assignments based on a discriminative training criterion.Training and decoding with the model requires summing over an exponential number of hidden-variable assignments: the required summations can be computed efficiently and exactly using dynamic programming. | |
| tech,16-3-H05-1095,ak | <term> phrases </term> , as well as a <term> | training | method </term> based on the <term> maximization | #5630 A statistical translation model is also presented that deals such phrases, as well as atraining method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. | |
| lr,17-5-H05-1095,ak | allows to better generalize from the <term> | training | data </term> . This paper investigates some | #5675 Experimental results are presented, that demonstrate how the proposed method allows to better generalize from thetraining data. | |
| lr,15-1-P05-1046,ak | limited by the need for <term> supervised | training | data </term> . We demonstrate that for certain | #9032 The applicability of many current information extraction techniques is severely limited by the need for supervised training data. | |
| tech,8-1-P05-1069,ak | In this paper , we present a novel <term> | training | method </term> for a <term> localized phrase-based | #9937 In this paper, we present a noveltraining method for a localized phrase-based prediction model for statistical machine translation (SMT). |
