A Domain Specific ESA Method for Semantic Text Matching

Abstract

An approach to semantic text similarity matching is concept-based characterization of entities and themes that can be automatically extracted from content. This is useful to build an effective recommender system on top of similarity measures and its usage for document retrieval and ranking. In this work, our research goal is to create an expert system for education recommendation, based on skills, capabilities, areas of expertise present in someone's curriculum vitae and personal preferences. This form of semantic text matching challenge needs to take into account all the personal educational experiences (formal, informal, and on-the-job), but also work-related know-how, to create a concept based profile of the person. This will allow a reasoned matching process from CVs and career vision to descriptions of education programs. Taking inspiration from the explicit semantic analysis (ESA), we developed a domain-specific approach to semantically characterize short texts and to compare their content for semantic similarity. Thanks to an enriching and a filtering process, we transform the general purpose German Wikipedia into a domain specific model for our task. The domain is defined also through a German knowledge base or vocabulary of description for educational experiences and for job offers. Initial testing with a small set of documents demonstrated that our approach

A Bibliometric Perspective on AI Research for Job-Résumé Matching

“…A more recent study by [ 43 ] considers the similar problem of recommending educational programs based on the CV and personal preferences of candidates. To that end, they use explicit semantic analysis (ESA), a technique that represents domain-specific semantic concepts based on the Wikipedia entry pages related to that domain.…”

“…A domain-specific ESA method for semantic text matching [43] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6) A vectorisation model for job matching application of a government employment service office [44] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6) Competence-level prediction and resume and job description matching using context-aware transformer models [45] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6) description of this approach is provided in [39], emphasising the formalities of the semantic network construction procedure for the job and résumé models, along with similarity metrics for the matching. Another study by [41] also proposes a framework for the ranking of candidates that initially extracts relevant information from résumés via tokenisation and NER using the spaCy library.…”

Towards Granular Knowledge Structures: Comparison of Different Approaches

“…To form granules from concepts, an algorithm must be able to group them into fuzzy granules. 2) Building Granules: Grouping is based on the strength of relations between concepts [13]. Different use cases enforce different distance-measures to achieve good results [2].…”