| 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: | 978-3-030-85250-4 978-3-030-85251-1 |
| ISSN: | 0302-9743 1611-3349 |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/24567 |
| 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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Smith, E., Weiler, A., & Braschler, M. (2021). Skill extraction for domain-specific text retrieval in a job-matching platform [Conference paper]. In K. S. Candan, B. Ionescu, L. Goeuriot, B. Larsen, H. Müller, A. Joly, M. Maistro, F. Piroi, G. Faggioli, & N. Ferro (Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction (pp. 116–128). Springer. https://doi.org/10.1007/978-3-030-85251-1_10
Smith, E., Weiler, A. and Braschler, M. (2021) ‘Skill extraction for domain-specific text retrieval in a job-matching platform’, in K.S. Candan et al. (eds) Experimental IR Meets Multilinguality, Multimodality, and Interaction. Cham: Springer, pp. 116–128. Available at: https://doi.org/10.1007/978-3-030-85251-1_10.
E. Smith, A. Weiler, and M. Braschler, “Skill extraction for domain-specific text retrieval in a job-matching platform,” in Experimental IR Meets Multilinguality, Multimodality, and Interaction, 2021, pp. 116–128. doi: 10.1007/978-3-030-85251-1_10.
SMITH, Ellery, Andreas WEILER und Martin BRASCHLER, 2021. Skill extraction for domain-specific text retrieval in a job-matching platform. In: K. Selçuk CANDAN, Bogdan IONESCU, Lorraine GOEURIOT, Birger LARSEN, Henning MÜLLER, Alexis JOLY, Maria MAISTRO, Florina PIROI, Gugliemlo FAGGIOLI und Nicola FERRO (Hrsg.), Experimental IR Meets Multilinguality, Multimodality, and Interaction. Conference paper. Cham: Springer. 2021. S. 116–128. ISBN 978-3-030-85250-4
Smith, Ellery, Andreas Weiler, and Martin Braschler. 2021. “Skill Extraction for Domain-Specific Text Retrieval in a Job-Matching Platform.” Conference paper. In Experimental IR Meets Multilinguality, Multimodality, and Interaction, edited by K. Selçuk Candan, Bogdan Ionescu, Lorraine Goeuriot, Birger Larsen, Henning Müller, Alexis Joly, Maria Maistro, Florina Piroi, Gugliemlo Faggioli, and Nicola Ferro, 116–28. Cham: Springer. https://doi.org/10.1007/978-3-030-85251-1_10.
Smith, Ellery, et al. “Skill Extraction for Domain-Specific Text Retrieval in a Job-Matching Platform.” Experimental IR Meets Multilinguality, Multimodality, and Interaction, edited by K. Selçuk Candan et al., Springer, 2021, pp. 116–28, https://doi.org/10.1007/978-3-030-85251-1_10.
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