1,721,140 research outputs found
A Tree Kernel-Based Shallow A Tree Kernel Based Shallow Semantic Parser for Thematic Role Extraction
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Efficient linearization of tree kernel functions
No full textThe combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two ma-jor drawbacks: first, the implicit representa-tion of feature spaces does not allow us to un-derstand which features actually triggered the generalization; second, the resulting compu-tational burden may in some cases render un-feasible to use large data sets for training. We propose an approach based on feature space reverse engineering to tackle both problems. Our experiments with Tree Kernels on a Se-mantic Role Labeling data set show that the proposed approach can drastically reduce the computational footprint while yielding almost unaffected accuracy.
Reverse engineering of tree kernel feature spaces
No full textWe present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernel-based machine, e.g. Support Vector Ma-chines (SVMs). In particular, our min-ing algorithm selects the most relevant fea-tures based on SVM estimated weights and uses this information to automatically infer an explicit representation of the in-put data. The explicit features (a) improve our knowledge on the target problem do-main and (b) make large-scale learning practical, improving training and test time, while yielding accuracy in line with tradi-tional TK classifiers. Experiments on se-mantic role labeling and question classifi-cation illustrate the above claims.
CUNIT: A Semantic Role Labeling System for Modern Standard Arabic
No full textIn this paper, we present a system for Ara-bic semantic role labeling (SRL) based on SVMs and standard features. The system is evaluated on the released SEMEVAL 2007 development and test data. The results show an Fβ=1 score of 94.06 on argument bound-ary detection and an overall Fβ=1 score of 81.43 on the complete semantic role label-ing task using gold parse trees.
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