In Natural Language Processing, entity linking (EL) has centered around
Wikipedia, but yet remains underexplored for the job market domain.
Disambiguating skill mentions can help us get insight into the current labor
market demands. In this work, we are the first to explore EL in this domain,
specifically targeting the linkage of occupational skills to the ESCO taxonomy
(le Vrang et al., 2014). Previous efforts linked coarse-grained (full)
sentences to a corresponding ESCO skill. In this work, we link more
fine-grained span-level mentions of skills. We tune two high-performing neural
EL models, a bi-encoder (Wu et al., 2020) and an autoregressive model (Cao et
al., 2021), on a synthetically generated mention--skill pair dataset and
evaluate them on a human-annotated skill-linking benchmark. Our findings reveal
that both models are capable of linking implicit mentions of skills to their
correct taxonomy counterparts. Empirically, BLINK outperforms GENRE in strict
evaluation, but GENRE performs better in loose evaluation (accuracy@$k$).

本文首次探索在职业技能与 ESCO 分类之间进行实体链接，使用高效的神经模型（双编码器和自回归模型）进行技能链接的实证研究。

职场领域的实体链接

Entity Linking in the Job Market Domain

Skills play a central role in the job market and many human resources (HR)
processes. In the wake of other digital experiences, today's online job market
has candidates expecting to see the right opportunities based on their skill
set. Similarly, enterprises increasingly need to use data to guarantee that the
skills within their workforce remain future-proof. However, structured
information about skills is often missing, and processes building on self- or
manager-assessment have shown to struggle with issues around adoption,
completeness, and freshness of the resulting data. Extracting skills is a
highly challenging task, given the many thousands of possible skill labels
mentioned either explicitly or merely described implicitly and the lack of
finely annotated training corpora. Previous work on skill extraction overly
simplifies the task to an explicit entity detection task or builds on manually
annotated training data that would be infeasible if applied to a complete
vocabulary of skills. We propose an end-to-end system for skill extraction,
based on distant supervision through literal matching. We propose and evaluate
several negative sampling strategies, tuned on a small validation dataset, to
improve the generalization of skill extraction towards implicitly mentioned
skills, despite the lack of such implicit skills in the distantly supervised
data. We observe that using the ESCO taxonomy to select negative examples from
related skills yields the biggest improvements, and combining three different
strategies in one model further increases the performance, up to 8 percentage
points in RP@5. We introduce a manually annotated evaluation benchmark for
skill extraction based on the ESCO taxonomy, on which we validate our models.
We release the benchmark dataset for research purposes to stimulate further
research on the task.

本研究提出了一种基于远程监督的技能提取系统，结合 ESCO 分类法和多种负采样策略提高提取技能的性能，并引入手动注释的基准评估数据集，以促进该任务的进一步研究。