Lexicon-Driven Automatic Sentence Generation for the Skills Section in a Job Posting

Vera Aleksic, Mona Brems, Anna Mathes, Theresa Bertele


Abstract
This paper presents a sentence generation pipeline as implemented on the online job board Stepstone. The goal is to automatically create a set of sentences for the candidate profile and the task description sections in a job ad, related to a given input skill. They must cover two different “tone of voice” variants in German (Du, Sie), three experience levels (junior, mid, senior), and two optionality values (skill is mandatory or optional/nice to have). The generation process considers the difference between soft skills, natural language competencies and hard skills, as well as more specific sub-categories such as IT skills, programming languages and similar. To create grammatically consistent text, morphosyntactic features from the proprietary skill ontology and lexicon are consulted. The approach is a lexicon-driven generation process that compares all lexical features of the new input skills with the ones already added to the sentence database and creates new sentences according to the corresponding templates.
Anthology ID:
2023.ranlp-1.4
Volume:
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
Month:
September
Year:
2023
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
32–40
Language:
URL:
https://aclanthology.org/2023.ranlp-1.4
DOI:
Bibkey:
Cite (ACL):
Vera Aleksic, Mona Brems, Anna Mathes, and Theresa Bertele. 2023. Lexicon-Driven Automatic Sentence Generation for the Skills Section in a Job Posting. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 32–40, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
Lexicon-Driven Automatic Sentence Generation for the Skills Section in a Job Posting (Aleksic et al., RANLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.ranlp-1.4.pdf