Abstract
Deep Learning approach using probability distribution to natural language processing achieves significant accomplishment. However, natural languages have inherent linguistic structures rather than probabilistic distribution. This paper presents a new graph-based representation of syntactic structures called syntactic knowledge graph based on dependency relations. This paper investigates the valency theory and the markedness principle of natural languages to derive an appropriate set of dependency relations for the syntactic knowledge graph. A new set of dependency relations derived from the markers is proposed. This paper also demonstrates the representation of various linguistic structures to validate the feasibility of syntactic knowledge graphs.
Highlights
Linguistic intelligence is one of the ultimate goals of Natural Language Processing (NLP) in Artificial Intelligence (AI)
A formal way to define dependency relations based on the universality of natural languages and graph-based representation of linguistic structures is a significant issue in NLP
This paper proposed a new set of dependency relations based on the valency theory and the markedness principle
Summary
Linguistic intelligence is one of the ultimate goals of Natural Language Processing (NLP) in Artificial Intelligence (AI). A formal way to define dependency relations based on the universality of natural languages and graph-based representation of linguistic structures is a significant issue in NLP. The valency values and dependency relations are the cohesive principles to generate linguistic structures, and markedness is the apparatus to realize the grammatical functions of dependency relations in sentences. The government-dependency markers related to subcategorization are used to construct syntactic structures, while the attachment-restriction markers represent the optional modification relationships to impose additional semantic features. 4. Analysis of Dependency Relations by Markedness Principle The dependency relations play a vital role in analyzing syntactic/semantic structures of natural languages. This paper argues that the markers, whether they are government-dependency or attachment-restriction, play the governor's role to its associated constituent since the explicit markers define additional syntactic/semantic function for the associated. The markers are representative of the associated phrasal structure for representing syntactic/semantic functions
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