eXSTS: eXplainable Semantic Textual Similarity

Abstract

We propose eXplainable Semantic Textual Similarity (eXSTS) with two visualizations that allow the users to investigate the Semantic Textual Similarity (STS) relationship between two documents. eXSTS offers insights for the users who are not familiar with Natural Language Processing to the STS relationship between two documents. eXSTS was invented to deal with the job advertisement classification task. When a job advertisement is entered into eXSTS, eXSTS retrieves the five most relevant National Occupational Classification (NOC) unit group based on the document STS score of the job advertisement and each NOC unit group. The front-end user interface demonstrates the STS relationship between the job advertisement and one NOC unit group in the five most relevant NOC unit groups to explore the important STS information across the job advertisement and this NOC unit group and why eXSTS chose this NOC unit group to opt-in the five most relevant NOC unit groups.