The EcoLexicon Semantic Sketch Grammar: from Knowledge Patterns to Word Sketches

TL;DR

This paper introduces the EcoLexicon Semantic Sketch Grammar, a tool integrated into Sketch Engine that automates the extraction of semantic relations from English corpora, aiding terminologists in semantic analysis.

Contribution

It presents the development and evaluation of a knowledge pattern-based sketch grammar for semantic relation extraction, enhancing user-friendly access to semantic contexts.

Findings

Initial evaluation shows promising precision and recall metrics.

Refinement of rules improves accuracy and reduces noise.

The tool enables domain-independent semantic relation extraction.

Abstract

Many projects have applied knowledge patterns (KPs) to the retrieval of specialized information. Yet terminologists still rely on manual analysis of concordance lines to extract semantic information, since there are no user-friendly publicly available applications enabling them to find knowledge rich contexts (KRCs). To fill this void, we have created the KP-based EcoLexicon Semantic SketchGrammar (ESSG) in the well-known corpus query system Sketch Engine. For the first time, the ESSG is now publicly available inSketch Engine to query the EcoLexicon English Corpus. Additionally, reusing the ESSG in any English corpus uploaded by the user enables Sketch Engine to extract KRCs codifying generic-specific, part-whole, location, cause and function relations, because most of the KPs are domain-independent. The information is displayed in the form of summary lists (word sketches) containing the pairs of terms linked by a given semantic relation. This paper describes the process of building a KP-based sketch grammar with special focus on the last stage, namely, the evaluation with refinement purposes. We conducted an initial shallow precision and recall evaluation of the 64 English sketch grammar rules created so far for hyponymy, meronymy and causality. Precision was measured based on a random sample of concordances extracted from each word sketch type. Recall was assessed based on a random sample of concordances where known term pairs are found. The results are necessary for the improvement and refinement of the ESSG. The noise of false positives helped to further specify the rules, whereas the silence of false negatives allows us to find useful new patterns.