S15-1034 knowledge . This study focuses on the automatic feature engineering task . We start from atomic features
E14-1070 we rely on machine learning and automatic feature engineering with tree kernels . We used our
D13-1044 tree kernels , are used to handle automatic feature engineering . It is enough to specify the
W06-2909 relations , i.e. we carry out an automatic feature engineering process . To validate our approach
D11-1066 these are excellent tools for automatic feature engineering , especially for unknown tasks
W06-2909 kernels are very promising for automatic feature engineering , especially when the available
D13-1044 systems ) are removed . <title> Automatic Feature Engineering for Answer Selection and Extraction
P15-1097 to generic trees and graphs . Automatic feature engineering using structural kernels requires
W11-2606 years , a variety of manual and automatic feature engineering techniques have been developed
P12-1080 structure subparts , we rely on automatic feature engineering via structural ker - nels . For
D13-1044 previous work by applying the idea of automatic feature engineering with tree kernels to answer extraction
W06-2607 simple tree manipulations trigger automatic feature engineering that highly improves accuracy
W13-3509 approach where tree kernels handle automatic feature engineering . In particular , to detect the
D13-1044 approach where tree kernels handle automatic feature engineering . To build an automatic Question
D11-1096 similarity ) . On the other hand , automatic feature engineering of syntactic or shallow semantic
W11-2606 problematic in this respect . 2.3 Automatic Feature Engineering In recent years , a variety of
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