RTV: Tree Kernels for Thematic Role Classification
نویسندگان
چکیده
We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. The resulting system has an F1 of 75.44 on the SemEval2007 closed task on semantic role labeling.
منابع مشابه
A Tree Kernel-Based Shallow Semantic Parser for Thematic Role Extraction
We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling sy...
متن کاملSyntactic Kernels for Natural Language Learning: the Semantic Role Labeling Case
In this paper, we use tree kernels to exploit deep syntactic parsing information for natural language applications. We study the properties of different kernels and we provide algorithms for their computation in linear average time. The experiments with SVMs on the task of predicate argument classification provide empirical data that validates our methods.
متن کاملEfficient Convolution Kernels for Dependency and Constituent Syntactic Trees
In this paper, we provide a study on the use of tree kernels to encode syntactic parsing information in natural language learning. In particular, we propose a new convolution kernel, namely the Partial Tree (PT) kernel, to fully exploit dependency trees. We also propose an efficient algorithm for its computation which is futhermore sped-up by applying the selection of tree nodes with non-null k...
متن کاملSemantic Role Recognition Using Kernels on Weighted Marked Ordered Labeled Trees
We present a method for recognizing semantic role arguments using a kernel on weighted marked ordered labeled trees (the WMOLT kernel). We extend the kernels on marked ordered labeled trees (Kazama and Torisawa, 2005) so that the mark can be weighted according to its importance. We improve the accuracy by giving more weights on subtrees that contain the predicate and the argument nodes with thi...
متن کاملTree Kernel Engineering in Semantic Role Labeling Systems
Recent work on the design of automatic systems for semantic role labeling has shown that feature engineering is a complex task from a modeling and implementation point of view. Tree kernels alleviate such complexity as kernel functions generate features automatically and require less software development for data extraction. In this paper, we study several tree kernel approaches for both bounda...
متن کامل