Entity Linking in the Job Market Domain (2024)

Abstract: 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).

## introduction labor market dynamics, influenced by technological changes, migration, and digitization, have led to the availability of job descriptions (jd) on platforms to attract qualified candidates @xcite @xcite . it is important to extract and link surface form skills to a unique taxonomy entry, allowing us to quantify the current labor market dynamics and determine the demands and needs. we attempt to tackle the problem of entity linking (el) in the job market domain, specifically the linking of fine-grained span-level skill mentions to a specific taxonomy entry. generally, el is the task of linking mentions of entities in unstructured text documents to their respective unique entities in a knowledge base (kb), most commonly wikipedia @xcite . recent models address this problem by producing entity representations from a (sub)set of kb information, e.g., entity descriptions @xcite , fine-grained entity types @xcite @xcite , or generation of the input text autoregressively @xcite . for skill linking specifically, we use the european skills, competences, qualifications and occupations (esco; le @xcite taxonomy due to its comprehensiveness. previous work classified spans to its taxonomy code via multi-class classification @xcite without surrounding context and neither the full breadth of esco. @xcite approaches it as a sequence labeling task, but only uses more coarsegrained esco concepts, and not the full taxonomy. last, others attempt to match the full sentence to their respective taxonomy title @xcite @xcite . the latter comes with a limitation: the taxonomy title does not indicate which subspan in the sentence it points to, without an exact match. we define this as an implicit skill, where mentions (spans) in the sentence do not have an exact string match with a skill in the esco taxonomy. the differences can range from single tokens to entire phrases. for example, we can link "being able to work together" to "plan teamwork". 2 if we know the exact span, this implicit skill can be added to the taxonomy as an alternative choice for the surface skill. as a result, this gives us a more nuanced view of the labor market skill demands. therefore, we attempt to train models to the linking of both implicit and explicit skill mentions. ## methodology definition. in el, we process the input document @xmath0, . . . , w r }, a collection of entity mentions denoted as @xmath1, . . . , m n }, and a kb, esco in our case: @xmath2, . . . , e 13890 , unk}. the objective of an el model is to generate a list of mention-entity pairs {(m i , e i )} n @xmath3, where each entity e corresponds to an entry in a kb. we assume that both the titles and descriptions of the entities are available, which is a common scenario in el research @xcite @xcite . we also assume that each mention in the document has a corresponding valid gold entity present in the knowledge base, including unk. this scenario is typically referred to as "in-kb evaluation". similar to prior research efforts @xcite , we also presuppose that the mentions within the document have already been tagged. ## results mention blink genre 1 work in a way that is patient-centred and inclusive. person centred care (k0913) work in an organised manner (t) 2 you can ride a bike. sell bicycles (s1.6.1) drive two-wheeled vehicles (s8.2.2) 3 it is expected that you are a super user of the ms office tools. use microsoft office (s5.6.1) tools for software configuration management (0613) 4 picking and packing. carry out specialised packing for customers (s6.1.3) perform loading and unloading operations (s6.2.1) ## discussion qualitative analysis. we manually inspected a subset of the predictions. we present qualitative examples in evaluation limitation. we qualitatively demonstrate the linking of skills that are implicit and/or valid. empirically, we observe that the strict linking of skills leads to an underestimation of model performance. we believe this limitation is rooted in evaluation. in train, dev, and test, there is only one correct gold label. we reciprocate the findings by @xcite , where they found that a large number of predictions are "technically correct" but limitations in wikipedia-based evaluation falsely penalized their model (i.e., a more or less precise version of the same entity). especially 5 in ## conclusion we present entity linking in the job market domain, using two existing high-performing neural models. we demonstrate that the bi-encoder architecture of blink is more suited to the job market domain compared to the autoregressive genre model. while strict linking results favor blink over genre, if we relax the number of candidates, we observe that genre performs slightly better. from a qualitative perspective, the performance of strict linking results is modest due to limitations in the evaluation set, which considers only one skill correct per mention. however, upon examining the predictions, we identify valid links, suggesting the possibility of multiple correct links for a particular mention, highlighting the need for more comprehensive evaluation. we hope this work sparks interest in entity linking within the job market domain.