| D13-1073 | hybrid approach , augmenting a | machine learner | to a set of handwritten rules |
| H05-1046 | tagged data was used to train the | machine learner | . We compared this method with |
| E14-4022 | incorporate the two feature spaces in a | machine learner | . 6 Acknowledgements This work |
| D12-1053 | van den Bosch , 2005 ) . This | machine learner | is originally designed for part-of-speech |
| C04-1035 | algorithms on these data sets . The rst | machine learner | used , SLIPPER , extracts optimised |
| I05-3003 | noted that we use a different | machine learner | from the original method ( Nivre |
| I05-3003 | leaves the decision to another | machine learner | that makes use of global fea |
| C04-1035 | 2003 ) . As with all memory-based | machine learners | , TiMBL stores representations |
| C02-1088 | also made in other languages . As | machine learner | requires training and test examples |
| I05-3003 | Similarly to Isozaki 's work , we use | machine learner | ( SVMs ) to construct the root |
| D15-1035 | the loss function of a POS-tag | machine learner | , resulting in improved performance |
| D08-1004 | annotator can provide hints to a | machine learner | by highlighting contextual " |
| I05-3003 | and then gives these features to | machine learner | . The machine learner uses the |
| D12-1053 | memory-based system except that no | machine learner | is included . The most frequent |
| D10-1084 | effectiveness of various features and | machine learners | for this task . While a simple |
| H05-1046 | tagged data was used to train a | machine learner | , which disambiguated toponyms |
| E09-1070 | Wikipedia-based feature in their | machine learner | . Such approaches are limited |
| J08-3001 | disagreements add patterns for the | machine learner | to find . Unfortunately , the |
| D12-1064 | employ a maximum entropy ( MaxEnt ) | machine learner | -- MegaM ( fifth re - lease ) |
| C02-1088 | individual result . In our module , | machine learners | train with the training examples |
