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

kLogNLP: Graph kernel–based relational learning of natural language / Mathias Verbeke; Paolo Frasconi; Kurt De Grave; Fabrizio Costa; Luc De Raedt. - STAMPA. - (2014), pp. 85-90. (Intervento presentato al convegno Annual Meeting of the Association for Computational Lingusitics (ACL) tenutosi a Baltimore).

kLogNLP: Graph kernel–based relational learning of natural language

FRASCONI, PAOLO;
2014

Abstract

kLog is a framework for kernel-based learning that has already proven success- ful in solving a number of relational tasks in natural language processing. In this pa- per, we present kLogNLP, a natural lan- guage processing module for kLog. This module enriches kLog with NLP-specific preprocessors, enabling the use of exist- ing libraries and toolkits within an elegant and powerful declarative machine learn- ing framework. The resulting relational model of the domain can be extended by specifying additional relational features in a declarative way using a logic program- ming language. This declarative approach offers a flexible way of experimentation and a way to insert domain knowledge.
2014
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics
Annual Meeting of the Association for Computational Lingusitics (ACL)
Baltimore
Mathias Verbeke; Paolo Frasconi; Kurt De Grave; Fabrizio Costa; Luc De Raedt
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/952200
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 1
social impact