Publication type: Conference paper
Type of review: Peer review (publication)
Title: Skill extraction for domain-specific text retrieval in a job-matching platform
Authors: Smith, Ellery
Weiler, Andreas
Braschler, Martin
et. al: No
DOI: 10.1007/978-3-030-85251-1_10
Proceedings: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Editors of the parent work: Candan, K. Selçuk
Ionescu, Bogdan
Goeuriot, Lorraine
Larsen, Birger
Müller, Henning
Joly, Alexis
Maistro, Maria
Piroi, Florina
Faggioli, Gugliemlo
Ferro, Nicola
Page(s): 116
Pages to: 128
Conference details: 12th International Conference of the CLEF Association (CLEF 2021), virtual event, 21–24 September 2021
Issue Date: 2021
Series: Lecture Notes in Computer Science
Series volume: 12880
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-85250-4
ISSN: 0302-9743
Language: English
Subjects: Information retrieval; Domain-specific retrieval; Term extraction; Natural language processing
Subject (DDC): 006: Special computer methods
Abstract: We discuss a domain-specific retrieval application for matching job seekers with open positions that uses a novel syntactic method of extracting skill-terms from the text of natural language job advertisements. Our new method is contrasted with two word embeddings methods, using word2vec. We define the notion of a skill headword, and present an algorithm that learns syntactic dependency patterns to recognize skill-terms. In all metrics, our syntactic method outperforms both word embeddings methods. Moreover, the word embeddings approaches were unable to model a meaningful distinction between skill-terms and non-skill-terms, while our syntactic approach was able to perform this successfully. We also show how these extracted skills can be used to automatically construct a semantic job-skills ontology, and facilitate a job-to-candidate matching system.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: Skillue - Digitaler Marktplatz für Fähigkeiten und Marktwerte
Appears in collections:Publikationen School of Engineering

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