{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-representations-for-soft-skill","title":"Learning Representations for Soft Skill Matching","arxiv_id":"1807.07741","date":"2018-07-20","proceeding":null,"authors":["Luiza Sayfullina","Eric Malmi","Juho Kannala"],"abstract":"Employers actively look for talents having not only specific hard skills but\nalso various soft skills. To analyze the soft skill demands on the job market,\nit is important to be able to detect soft skill phrases from job advertisements\nautomatically. However, a naive matching of soft skill phrases can lead to\nfalse positive matches when a soft skill phrase, such as friendly, is used to\ndescribe a company, a team, or another entity, rather than a desired candidate.\n  In this paper, we propose a phrase-matching-based approach which\ndifferentiates between soft skill phrases referring to a candidate vs.\nsomething else. The disambiguation is formulated as a binary text\nclassification problem where the prediction is made for the potential soft\nskill based on the context where it occurs. To inform the model about the soft\nskill for which the prediction is made, we develop several approaches,\nincluding soft skill masking and soft skill tagging.\n  We compare several neural network based approaches, including CNN, LSTM and\nHierarchical Attention Model. The proposed tagging-based input representation\nusing LSTM achieved the highest recall of 83.92% on the job dataset when fixing\na precision to 95%.","url_abs":"http://arxiv.org/abs/1807.07741v1","url_pdf":"http://arxiv.org/pdf/1807.07741v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-representations-for-soft-skill","repo_url":"https://github.com/muzaluisa/soft-skill-matching","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"learning-representations-for-soft-skill","repo_url":"https://github.com/muzaluisa/Learning_Representations_for_Soft_Skill_Matching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"binary-text-classification","task_name":"Binary text classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.07741","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}