A Corpus-based Evaluation of a Domain-specific Text to Knowledge Mapping Prototype

TL;DR

This study evaluates a domain-specific Text to Knowledge Mapping prototype for DC electrical circuits using a newly developed corpus, focusing on lexical and semantic components to improve NLP understanding in this specialized domain.

Contribution

The paper introduces a new annotated corpus and a domain-specific semantic relation framework to enhance the prototype’s ability to map instructional texts onto knowledge structures.

Findings

Prototype successfully parsed a significant portion of corpus sentences.

Development of 55 semantic relations, including 42 with inverse relations.

Corpus analysis confirmed representativeness and coverage of discourse.

Abstract

The aim of this paper is to evaluate a Text to Knowledge Mapping (TKM) Prototype. The prototype is domain-specific, the purpose of which is to map instructional text onto a knowledge domain. The context of the knowledge domain is DC electrical circuit. During development, the prototype has been tested with a limited data set from the domain. The prototype reached a stage where it needs to be evaluated with a representative linguistic data set called corpus. A corpus is a collection of text drawn from typical sources which can be used as a test data set to evaluate NLP systems. As there is no available corpus for the domain, we developed and annotated a representative corpus. The evaluation of the prototype considers two of its major components- lexical components and knowledge model. Evaluation on lexical components enriches the lexical resources of the prototype like vocabulary and grammar structures. This leads the prototype to parse a reasonable amount of sentences in the corpus. While dealing with the lexicon was straight forward, the identification and extraction of appropriate semantic relations was much more involved. It was necessary, therefore, to manually develop a conceptual structure for the domain to formulate a domain-specific framework of semantic relations. The framework of semantic relationsthat has resulted from this study consisted of 55 relations, out of which 42 have inverse relations. We also conducted rhetorical analysis on the corpus to prove its representativeness in conveying semantic. Finally, we conducted a topical and discourse analysis on the corpus to analyze the coverage of discourse by the prototype.