Papers by Rob Goot
Where are we Still Split on Tokenization? (2024.findings-eacl)
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| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
| Approach: | They propose an efficient method for tokenization with subword-based language models and evaluate it on 122 languages in 20 scripts. |
| Outcome: | The proposed method performs on par with the state-of-the-art on 122 languages in 20 scripts. |
Entity Linking in the Job Market Domain (2024.findings-eacl)
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| Challenge: | In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain. |
| Approach: | They propose to use a bi-encoder and an autoregressive model to link fine-grained span-level skill mentions to a specific taxonomy entry to quantify labor market demands. |
| Outcome: | The proposed model outperforms GENRE in strict evaluation, but performs better in loose evaluation. |
NNOSE: Nearest Neighbor Occupational Skill Extraction (2024.eacl-long)
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| Challenge: | a new method for extracting occupational skills from text is needed to overcome the scarcity of skills across datasets. |
| Approach: | They propose a method that leverages multiple datasets to extract occupational skills from text . they propose NNOSE to extract neighboring skills from other datasets . |
| Outcome: | The proposed method improves infrequent skill extraction without additional fine-tuning. |