# Learning Representations for Soft Skill Matching

arXiv: https://arxiv.org/abs/1807.07741 · HTML: https://mlsanity.com/paper/1807.07741/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2018.

## Built on by (most cited first, 19 of 19)

- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction](https://mlsanity.com/paper/2209.05987/) (2022)
- [Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers](https://mlsanity.com/paper/2307.03539/) (2023)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings](https://mlsanity.com/paper/2402.05617/) (2024)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)
- [Skill Extraction from Job Postings using Weak Supervision](https://mlsanity.com/paper/2209.08071/) (2022)
- [SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model](https://mlsanity.com/paper/2304.11060/) (2023)
- [Rethinking Skill Extraction in the Job Market Domain using Large Language Models](https://mlsanity.com/paper/2402.03832/) (2024)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- ["FIJO": a French Insurance Soft Skill Detection Dataset](https://mlsanity.com/paper/2204.05208/) (2022)
- [NNOSE: Nearest Neighbor Occupational Skill Extraction](https://mlsanity.com/paper/2401.17092/) (2024)
- [Efficient Text Encoders for Labor Market Analysis](https://mlsanity.com/paper/2505.24640/) (2025)
- [Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings](https://mlsanity.com/paper/2401.05073/) (2024)
- [Joint Extraction and Classification of Danish Competences for Job Matching](https://mlsanity.com/paper/2410.22103/) (2024)
- [Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker](https://mlsanity.com/paper/2511.07969/) (2025)
- [Tec-Habilidad: Skill Classification for Bridging Education and Employment](https://mlsanity.com/paper/2503.03932/) (2025)
- [NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching](https://mlsanity.com/paper/2506.19058/) (2025)
- [Leveraging LLMs For Turkish Skill Extraction](https://mlsanity.com/paper/2601.22885/) (2026)

## Abstract (arXiv)

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%.