By R. R. Jordan
Educational Writing path is designed for college kids embarking on additional experiences throughout the medium of English. This profitable direction is appropriate for college students at Cambridge First certificates point and above.
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Extra info for Academic Writing Course (3rd Edition)
When moving further to feature set 3 (which includes a higher number of low frequency PP features) KNN worsens clearly (c. 5% in accuracy and F-measure) while the other methods perform similarly. With sparser feature sets 4-5, KNN, PC SVM and ME show similar performance than with feature set 3 (the changes in SVM and ME are not statistically significant). 5 F-measure). 9 F-measure. 4σ and 2σ level, respectively. The resampling results in table 3 reveal that some classifiers perform worse than others when less training data is available3 .
Class-based construction of a verb lexicon. In: AAAI/IAAI, pp. 691–696 (2000) 16. : Automatic extraction of subcategorization from corpora. In: Proceedings of the 5th ACL Conference on Applied Natural Language Processing, Washington DC, pp. 356–363 (1997) 17. : Robust accurate statistical annotation of general text. In: Proceedings of the 3rd LREC, Las Palmas, Gran Canaria, pp. 1499–1504 (2002) 18. : Large lexicons for natural language processing: utilising the grammar coding system of ldoce.
The closest comparison point is the recent experiment reported in  which involved classifying 835 English 26 L. Sun, A. Korhonen, and Y. Krymolowski verbs to 14 Levin classes using SVM. Features were specifically selected via analysis of alternations that are used to characterize Levin classes. Both (i) shallow syntactic features (syntactic slots obtained using a chunker) and (ii) deep ones (SCFs extracted using Briscoe and Carroll’s system) were used. The accuracy was 58% with (i) and only 38% with (ii).