Learning representations for soft skill matching

Luiza Sayfullina; Eric Malmi; Juho Kannala

doi:10.1007/978-3-030-11027-7_15

Learning representations for soft skill matching

Luiza Sayfullina^*
, Eric Malmi
, Juho Kannala

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

32 Citations (Scopus)

272 Downloads (Pure)

Abstract

Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false positive matches when a soft skill phrase, such as friendly, is used to describe a company, a team, or another entity, rather than a desired candidate. In this paper, we propose a phrase-matching-based approach which differentiates between soft skill phrases referring to a candidate vs. something else. The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs. To inform the model about the soft skill for which the prediction is made, we develop several approaches, including soft skill masking and soft skill tagging. We compare several neural network based approaches, including CNN, LSTM and Hierarchical Attention Model. The proposed tagging-based input representation using LSTM achieved the highest recall of 83.92% on the job dataset when fixing a precision to 95%.

Original language	English
Title of host publication	Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers
Editors	Alexander Panchenko, Wil M. van der Aalst, Michael Khachay, Panos M. Pardalos, Vladimir Batagelj, Natalia Loukachevitch, Goran Glavaš, Dmitry I. Ignatov, Sergei O. Kuznetsov, Olessia Koltsova, Irina A. Lomazova, Andrey V. Savchenko, Amedeo Napoli, Marcello Pelillo
Publisher	Springer
Pages	141-152
Number of pages	12
ISBN (Print)	9783030110260
DOIs	https://doi.org/10.1007/978-3-030-11027-7_15
Publication status	Published - 1 Jan 2018
MoE publication type	A4 Conference publication
Event	International Conference on Analysis of Images, Social Networks and Texts - Moscow, Russian Federation Duration: 5 Jul 2018 → 7 Jul 2018 Conference number: 7

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11179 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Analysis of Images, Social Networks and Texts
Abbreviated title	AIST
Country/Territory	Russian Federation
City	Moscow
Period	05/07/2018 → 07/07/2018

Access to Document

10.1007/978-3-030-11027-7_15

Sayfullina.et.al.Learning.Representations.1807.07741Accepted author manuscript, 222 KB

http://arxiv.org/abs/1807.07741

Cite this

Sayfullina, L., Malmi, E., & Kannala, J. (2018). Learning representations for soft skill matching. In A. Panchenko, W. M. van der Aalst, M. Khachay, P. M. Pardalos, V. Batagelj, N. Loukachevitch, G. Glavaš, D. I. Ignatov, S. O. Kuznetsov, O. Koltsova, I. A. Lomazova, A. V. Savchenko, A. Napoli, & M. Pelillo (Eds.), Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers (pp. 141-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11179 LNCS). Springer. https://doi.org/10.1007/978-3-030-11027-7_15

Sayfullina, Luiza ; Malmi, Eric ; Kannala, Juho. / Learning representations for soft skill matching. Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. editor / Alexander Panchenko ; Wil M. van der Aalst ; Michael Khachay ; Panos M. Pardalos ; Vladimir Batagelj ; Natalia Loukachevitch ; Goran Glavaš ; Dmitry I. Ignatov ; Sergei O. Kuznetsov ; Olessia Koltsova ; Irina A. Lomazova ; Andrey V. Savchenko ; Amedeo Napoli ; Marcello Pelillo. Springer, 2018. pp. 141-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Learning representations for soft skill matching",

abstract = "Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false positive matches when a soft skill phrase, such as friendly, is used to describe a company, a team, or another entity, rather than a desired candidate. In this paper, we propose a phrase-matching-based approach which differentiates between soft skill phrases referring to a candidate vs. something else. The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs. To inform the model about the soft skill for which the prediction is made, we develop several approaches, including soft skill masking and soft skill tagging. We compare several neural network based approaches, including CNN, LSTM and Hierarchical Attention Model. The proposed tagging-based input representation using LSTM achieved the highest recall of 83.92\% on the job dataset when fixing a precision to 95\%.",

author = "Luiza Sayfullina and Eric Malmi and Juho Kannala",

year = "2018",

month = jan,

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doi = "10.1007/978-3-030-11027-7\_15",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "141--152",

editor = "Alexander Panchenko and \{van der Aalst\}, \{Wil M.\} and Michael Khachay and Pardalos, \{Panos M.\} and Vladimir Batagelj and Natalia Loukachevitch and Goran Glava{\v s} and Ignatov, \{Dmitry I.\} and Kuznetsov, \{Sergei O.\} and Olessia Koltsova and Lomazova, \{Irina A.\} and Savchenko, \{Andrey V.\} and Amedeo Napoli and Marcello Pelillo",

booktitle = "Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers",

address = "Germany",

note = "International Conference on Analysis of Images, Social Networks and Texts, AIST ; Conference date: 05-07-2018 Through 07-07-2018",

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Sayfullina, L, Malmi, E & Kannala, J 2018, Learning representations for soft skill matching. in A Panchenko, WM van der Aalst, M Khachay, PM Pardalos, V Batagelj, N Loukachevitch, G Glavaš, DI Ignatov, SO Kuznetsov, O Koltsova, IA Lomazova, AV Savchenko, A Napoli & M Pelillo (eds), Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11179 LNCS, Springer, pp. 141-152, International Conference on Analysis of Images, Social Networks and Texts, Moscow, Russian Federation, 05/07/2018. https://doi.org/10.1007/978-3-030-11027-7_15

Learning representations for soft skill matching. / Sayfullina, Luiza; Malmi, Eric ; Kannala, Juho.
Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. ed. / Alexander Panchenko; Wil M. van der Aalst; Michael Khachay; Panos M. Pardalos; Vladimir Batagelj; Natalia Loukachevitch; Goran Glavaš; Dmitry I. Ignatov; Sergei O. Kuznetsov; Olessia Koltsova; Irina A. Lomazova; Andrey V. Savchenko; Amedeo Napoli; Marcello Pelillo. Springer, 2018. p. 141-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11179 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Sayfullina, Luiza

AU - Malmi, Eric

AU - Kannala, Juho

N1 - Conference code: 7

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N2 - Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false positive matches when a soft skill phrase, such as friendly, is used to describe a company, a team, or another entity, rather than a desired candidate. In this paper, we propose a phrase-matching-based approach which differentiates between soft skill phrases referring to a candidate vs. something else. The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs. To inform the model about the soft skill for which the prediction is made, we develop several approaches, including soft skill masking and soft skill tagging. We compare several neural network based approaches, including CNN, LSTM and Hierarchical Attention Model. The proposed tagging-based input representation using LSTM achieved the highest recall of 83.92% on the job dataset when fixing a precision to 95%.

AB - Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false positive matches when a soft skill phrase, such as friendly, is used to describe a company, a team, or another entity, rather than a desired candidate. In this paper, we propose a phrase-matching-based approach which differentiates between soft skill phrases referring to a candidate vs. something else. The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs. To inform the model about the soft skill for which the prediction is made, we develop several approaches, including soft skill masking and soft skill tagging. We compare several neural network based approaches, including CNN, LSTM and Hierarchical Attention Model. The proposed tagging-based input representation using LSTM achieved the highest recall of 83.92% on the job dataset when fixing a precision to 95%.

UR - https://www.scopus.com/pages/publications/85059931431

UR - http://arxiv.org/abs/1807.07741

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DO - 10.1007/978-3-030-11027-7_15

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SN - 9783030110260

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A2 - Pardalos, Panos M.

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A2 - Loukachevitch, Natalia

A2 - Glavaš, Goran

A2 - Ignatov, Dmitry I.

A2 - Kuznetsov, Sergei O.

A2 - Koltsova, Olessia

A2 - Lomazova, Irina A.

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Sayfullina L, Malmi E , Kannala J. Learning representations for soft skill matching. In Panchenko A, van der Aalst WM, Khachay M, Pardalos PM, Batagelj V, Loukachevitch N, Glavaš G, Ignatov DI, Kuznetsov SO, Koltsova O, Lomazova IA, Savchenko AV, Napoli A, Pelillo M, editors, Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Springer. 2018. p. 141-152. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11027-7_15

Learning representations for soft skill matching

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