Tree Kernels for Semantic Role Labeling
نویسندگان
چکیده
منابع مشابه
Tree Kernels for Semantic Role Labeling
The availability of large scale data sets of manually annotated predicate–argument structures has recently favored the use of machine learning approaches to the design of automated semantic role labeling (SRL) systems. The main research in this area relates to the design choices for feature representation and for effective decompositions of the task in different learning models. Regarding the f...
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We compare different parse tree representations for the task of Chinese Semantic Role Labeling (SRL), including dependency and constituency parse trees, two tree pruning methods, and neighbor features. Three learning models are compared. By using SVM classifier with neighbor features and pruning tree to phrase level we achieve significantly better speed and accuracy than state of the art Chines...
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Semantic Role Labelling (SRL) is a complex Natural Language Processing (NLP) task that has received a lot of attention in the latest years. An accurate shallow semantic parser, that recognized predicate-argument structures in a sentence and assigned each argument a semantic (or thematic) role, could be a key factor of larger NLP architectures, human-machine interaction (e. g. high level, semant...
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Recently, many researches in natural language learning have considered the representation of complex linguistic phenomena by means of structural kernels. In particular, tree kernels have been used to represent verbal subcategorization frame (SCF) information for predicate argument classification. As the SCF is a relevant clue to learn the relation between syntax and semantic, the classification...
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A hybrid convolution tree kernel is proposed in this paper to effectively model syntactic structures for semantic role labeling (SRL). The hybrid kernel consists of two individual convolution kernels: a Path kernel, which captures predicateargument link features, and a Constituent Structure kernel, which captures the syntactic structure features of arguments. Evaluation on the datasets of CoNLL...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2008
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli.2008.34.2.193