| N06-2018 | recognition performance , we use a | supervised machine-learning | based approach which is a standard |
| W07-2205 | grammar development . For example , | supervised machine-learning | approaches rely on large amounts |
| W06-3310 | Image Classification We explored | supervised machine-learning | methods to automatically classify |
| N06-2018 | recognition task . The obstacle of | supervised machine-learning | methods is the lack of the annotated |
| C04-1131 | knowledge source . Our approach uses | supervised machine-learning | techniques to automatically acquire |
| W02-1815 | this paper we report results of a | supervised machine-learning | approach to Chinese word segmentation |
| W10-4117 | to the related studies on QA , | supervised machine-learning | technique may be effective for |
| W02-1815 | results reported in previous work in | supervised machine-learning | approaches . In the third experiment |
| P10-1009 | the learning capability of the | supervised machine-learning | methods . Specifically , the |
| W02-1815 | competitive compared with other | supervised machine-learning | segmenters reported in previous |
| S15-2073 | Entity Recognition We applied a | supervised machine-learning | ap - proach , based on Conditional |
| W11-1913 | later section . 2 Related Work | Supervised machine-learning | approaches to coreference resolution |
| W03-1017 | Naive Bayes ' , a commonly used | supervised machine-learning | algorithm . This approach presupposes |
| W12-3802 | bag-of-words model which is then used by | supervised machine-learning | algorithms for polarity classification |
| W06-3310 | shown to out-perform many other | supervised machine-learning | systems for text categorization |
| W04-1220 | natural language processing , | supervised machine-learning | based approach is a kind of standard |
| S10-1076 | four related subtasks . We take a | supervised machine-learning | technique using Markov Logic |
| W06-3310 | Support Vector Machines We explored | supervised machine-learning | systems using Support Vector |
| W12-3409 | machines ( SVM ) are a well known | supervised machine-learning | algorithm for linear binary classification |
| P01-1070 | predictive model , built by applying | supervised machine-learning | tech - niques , which can be |
