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A method based on rules and machine learning for logic form identification in Spanish

  • F. MARTÍNEZ-SANTIAGO (a1), M. C. DÍAZ-GALIANO (a1), M. Á. GARCÍA-CUMBRERAS (a1) and A. MONTEJO-RÁEZ (a1)
Abstract
Abstract

Logic Forms (LF) are simple, first-order logic knowledge representations of natural language sentences. Each noun, verb, adjective, adverb, pronoun, preposition and conjunction generates a predicate. LF systems usually identify the syntactic function by means of syntactic rules but this approach is difficult to apply to languages with a high syntax flexibility and ambiguity, for example, Spanish. In this study, we present a mixed method for the derivation of the LF of sentences in Spanish that allows the combination of hard-coded rules and a classifier inspired on semantic role labeling. Thus, the main novelty of our proposal is the way the classifier is applied to generate the predicates of the verbs, while rules are used to translate the rest of the predicates, which are more straightforward and unambiguous than the verbal ones. The proposed mixed system uses a supervised classifier to integrate syntactic and semantic information in order to help overcome the inherent ambiguity of Spanish syntax. This task is accomplished in a similar way to the semantic role labeling task. We use properties extracted from the AnCora-ES corpus in order to train a classifier. A rule-based system is used in order to obtain the LF from the rest of the phrase. The rules are obtained by exploring the syntactic tree of the phrase and encoding the syntactic production rules. The LF algorithm has been evaluated by using shallow parsing with some straightforward Spanish phrases. The verb argument labeling task achieves 84% precision and the proposed mixed LFi method surpasses 11% a system based only on rules.

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This work has been partially funded by the ATTOS project (TIN2012-38536-C03-01) from the Spanish Government and the AORESCU project (TIC 07684) from the Andalucía Government.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

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Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
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