W. Daelemans, A. Van den Bosch, and J. Zavrel. 1999a. Forgetting exceptions is harmful in language learning. Machine Learning, Special issue on Natural Language Learning, 34:11--41. Home/Search Document Not in Database Summary Related Articles Check |
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A Default First Order Family Weight Determination Procedure.. - van Halteren (2000) (Correct)
....does not decrease on more than one part, i.e. some losses are accepted but only if they are localized. 4 Quality of the first order weights In order to determine the quality of the WPDV system, using first order weights as described above, I run a series of experiments, using tasks introduced by Daelemans et al. 1999): 3 The Part of speech tagging task (POS) is to determine a wordclass tag on the basis of disambiguated tags of two preceding tokens and undisambiguated tags for the focus and two following tokens. 4 5 features with 170 480 values; 169 classes; 837Kcase training; 2x105Kcase test. The ....
....with 170 480 values; 169 classes; 837Kcase training; 2x105Kcase test. The Grapheme to phoneme conversion with stress task (GS) is to determine the pronunciation of an English grapheme, including 3 I only give a rough description of the tasks here. For the exact details, I refer the reader to Daelemans et al. 1999). 4 For a overall WPDV approach to wordclass tagging, see van Halteren (2000b) 120 Table 1: Accuracies for the POS task (with the training set ah tested in leave one out mode) Weighting scheme Test set ah i j Comparison Naive Bayes 96.41 96.24 TiMBL (k=1) 97.83 97.79 Maccent ....
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W. Daelemans, A. Van den Bosch, and J. Zavrel. 1999. Forgetting exceptions is harmful in language learning. Machine Learning, Special issue on Natural Language Learning, 34:11--41.
Machine Learning and Natural Language Processing - Marquez (2000) (1 citation) (Correct)
....a general instance based algorithm that makes a compression of the base of examples into a tree based structure, IGTree [57] used later for classifying new examples. These trees have proved to reduce significantly the space requirements and to be very efficient and accurate in several domains [58], including: Phonology (stress, word pronunciation) and morphology 4 Linear threshold algorithms, like Winnow, are very simple on line learning algorithms for 2 class problems with binary (i.e. 0 1 valued) input features. To classify new examples, they simply calculate a weighted sum of input ....
.... HMMs ME TBL NNs Speech recognition and synthesis [8, 9] 97] 187] 55, 56, 15] 206, 121, 155, 113, 229] Morphology [14] PoS tagging [194, 189] 200, 132, 140, 141, 164, 136, 138, 137] 45, 54, 144] 101, 177] 21, 22, 23, 6, 186] 155, 201, 70, 199, 131] IBL LSM EC PoS tagging [61, 60, 90, 58] [188] 90, 24, 139, 2, 136] Table 1: References corresponding to some low level NLP tasks Table 2 contains the references about parsing (either shallow or general) and structural ambiguity resolution. Table 3 groups the references about semantic and discourse level NLP tasks, namely, sense ....
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W. Daelemans, A. van den Bosch, and J. Zavrel. Forgetting Exceptions is Harmful in Language Learning. Machine Learning, 34:11--41, 1999.
Using Induced Rules as Complex Features in Memory-Based.. - van den Bosch (2000) Self-citation (Van den bosch) (Correct)
....the segmentation of sentences into non recursive NPs. Veenstra, 1998) used the Base NP tag set as presented in (Ramshaw and Marcus, 1995) I for inside a Base NP, O for outside a Base NP, and B for the first word in a Base NP following another Base NP. See (Veenstra, 1998) for more details, and (Daelemans et al. 1999) for a series of experiments on the original data set from which we have used a randomly extracted 10 . Part of speech tagging (possm) the disambiguation of syntactic classes of words in particular contexts. We assume a tagger architecture that processes a sentence from a disambiguated left to ....
....the sequence VP NP PP (VP = verb phrase, NP = noun phrase, PP = prepositional phrase) The data consists of fourtuples of words, extracted from the Wall Street Journal Treebank. From the original data set, used by (Ratnaparkhi et al. 1994) Collins and Brooks, 1995) and (Zavrel et al. 1997) (Daelemans et al. 1999) took the train and test set together to form the particular data also used here. Table 2 lists the average (10 fold crossvalidation) accuracies, measured in percentages of correctly classified test instances, of ib1 ig, ripper, and rbm on these five tasks. The clearest overall pattern in this ....
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W. Daelemans, A. Van den Bosch, and J. Zavrel. 1999. Forgetting exceptions is harmful in language learning. Machine Learning, 34(1--3):11-- 43.
The Role of Algorithm Bias vs Information Source in.. - De Pauw, Daelemans (2000) (1 citation) Self-citation (Daelemans) (Correct)
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W. Daelemans, A. Van den Bosch, and J. Zavrel. 1999a. Forgetting exceptions is harmful in language learning. Machine Learning, Special issue on Natural Language Learning, 34:11--41.
Genetic Algorithms for Feature Relevance Assignment in.. - Kool, Daelemans, Zavrel Self-citation (Daelemans Zavrel) (Correct)
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W. Daelemans, A. van den Bosch, and J. Zavrel. 1999a. Forgetting exceptions is harmful in language learning. In Machine Learning, special issue on natural language learning, 34 , pp 11-43.
Weighted Probability Distribution Voting, an introduction - van Halteren (1999) (Correct)
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W. Daelemans, A. Van den Bosch, and J. Zavrel. 1999a. Forgetting exceptions is harmful in language learning. Machine Learning, Special issue on Natural Language Learning, 34:11--41.
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