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%.