kLogNLP: Graph Kernel-based Relational Learning of Natural Language

被引：0

作者：

Verbeke, Mathias ^{[1
]}

Frasconi, Paolo ^{[2
]}

De Grave, Kurt ^{[1
]}

Costa, Fabrizio ^{[3
]}

De Raedt, Luc ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Comp Sci, Leuven, Belgium

[2] Univ Firenze, Dipartimento Sistemi & Informat, Florence, Italy

[3] Albert Ludwigs Univ, Inst Informat, Freiburg, Germany

来源：

PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS | 2014年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

kLog is a framework for kernel-based learning that has already proven successful in solving a number of relational tasks in natural language processing. In this paper, we present kLogNLP, a natural language processing module for kLog. This module enriches kLog with NLP-specific preprocessors, enabling the use of existing libraries and toolkits within an elegant and powerful declarative machine learning framework. The resulting relational model of the domain can be extended by specifying additional relational features in a declarative way using a logic programming language. This declarative approach offers a flexible way of experimentation and a way to insert domain knowledge.

引用

页码：85 / 90

页数：6

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