C00-1066 doculnents . However , the previous learning algorithms have some problems . One of them
C02-1158 Example-Based MT systems using the learning algorithms require large amounts of translation
A00-2007 Abstract The performance of machine learning algorithms can be improved by combining
A00-2009 methodologies are compared . All learning algorithms represent the context of an ambiguous
C02-1103 learning . A wide range of supervised learning algorithms has been applied to this problem
C02-1074 learning . A wide range of supervised learning algorithms has been applied to this issue
C02-1130 chose to examine five different learning algorithms . Along with C4 .5 , we examined
C02-1130 to the question of appropriate learning algorithms for the task . We chose to examine
A00-1022 different linguistic preprocessing and learning algorithms and provide some interpretations
C04-1013 vary widely . We argue here that learning algorithms in NLP have certain special properties
A00-2007 representations we use and our machine learning algorithms . We conclude with an outline
C02-1130 was trained . 5.2 Experiment 2 : Learning Algorithms a validation set . Learners include
C02-1074 well-known algorithms . Among these learning algorithms , we focus on the Nearest Neighbor
C02-1088 combined by seven different machine learning algorithms outperformed the best individual
A00-2007 acceptable results with the other learning algorithms . Acknowledgements We would like
A00-2009 there is from using many different learning algorithms on the same data . This is especially
C00-2139 plain sentences are used in the learning algorithms and the resulting structure is
C02-1130 aware of much previous work using learning algorithms to perform more fine-grained
C02-1158 Example-Based MT systems based on learning algorithms , similar translationpairs must
C04-1035 sluices , and run two machine learning algorithms on these data sets . The rst
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