Learning representations for soft skill matching

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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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%.

Details

Original languageEnglish
Title of host publicationAnalysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers
EditorsAlexander 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
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Analysis of Images, Social Networks and Texts - Moscow, Russian Federation
Duration: 5 Jul 20187 Jul 2018
Conference number: 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11179 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Analysis of Images, Social Networks and Texts
Abbreviated titleAIST
CountryRussian Federation
CityMoscow
Period05/07/201807/07/2018

ID: 31931109