Using a Diathesis Model for Semantic Parsing

TL;DR

This paper introduces a semantic parsing method that uses a diathesis-based model to analyze unrestricted texts, achieving over 73% accuracy in identifying semantic case-roles by combining argument structures with semantic diatheses.

Contribution

The approach uniquely integrates diathesis models with syntactic-semantic pattern matching to improve semantic role identification in unrestricted texts.

Findings

Achieves over 73% accuracy in semantic role identification.

Effectively combines argument structures with semantic diatheses.

Uses an approximate tree pattern-matching algorithm.

Abstract

This paper presents a semantic parsing approach for unrestricted texts. Semantic parsing is one of the major bottlenecks of Natural Language Understanding (NLU) systems and usually requires building expensive resources not easily portable to other domains. Our approach obtains a case-role analysis, in which the semantic roles of the verb are identified. In order to cover all the possible syntactic realisations of a verb, our system combines their argument structure with a set of general semantic labelled diatheses models. Combining them, the system builds a set of syntactic-semantic patterns with their own role-case representation. Once the patterns are build, we use an approximate tree pattern-matching algorithm to identify the most reliable pattern for a sentence. The pattern matching is performed between the syntactic-semantic patterns and the feature-structure tree representing the morphological, syntactical and semantic information of the analysed sentence. For sentences assigned to the correct model, the semantic parsing system we are presenting identifies correctly more than 73% of possible semantic case-roles.