SLIMER-IT: Zero-Shot NER on Italian Language
Traditional approaches to Named Entity Recognition (NER) frame the task into
a BIO sequence labeling problem. Although these systems often excel in the
downstream task at hand, they require extensive annotated data and struggle to
generalize to out-of-distribution input domains and unseen entity types. On the
contrary, Large Language Models (LLMs) have demonstrated strong zero-shot
capabilities. While several works address Zero-Shot NER in English, little has
been done in other languages. In this paper, we define an evaluation framework
for Zero-Shot NER, applying it to the Italian language. Furthermore, we
introduce SLIMER-IT, the Italian version of SLIMER, an instruction-tuning
approach for zero-shot NER leveraging prompts enriched with definition and
guidelines. Comparisons with other state-of-the-art models, demonstrate the
superiority of SLIMER-IT on never-seen-before entity tags.
66f3c35b3c412b900bd2bb02
Andrew Zamai
claimed_verified
2024-09-25T18:24:16.438Z
65be17eb52edc430281c6a72
https://cdn-avatars.huggingface.co/v1/production/uploads/65be17eb52edc430281c6a72/ZeSxgA25KnJ8pqvvy6rdl.png
Andrew Zamai
user
andrewzamai
66f3c35b3c412b900bd2bb03
Leonardo Rigutini
66f3c35b3c412b900bd2bb04
Marco Maggini
66f3c35b3c412b900bd2bb05
Andrea Zugarini
SLIMER-IT: Zero-Shot NER on Italian Language
Traditional approaches to Named Entity Recognition (NER) frame the task into
a BIO sequence labeling problem. Although these systems often excel in the
downstream task at hand, they require extensive annotated data and struggle to
generalize to out-of-distribution input domains and unseen entity types. On the
contrary, Large Language Models (LLMs) have demonstrated strong zero-shot
capabilities. While several works address Zero-Shot NER in English, little has
been done in other languages. In this paper, we define an evaluation framework
for Zero-Shot NER, applying it to the Italian language. Furthermore, we
introduce SLIMER-IT, the Italian version of SLIMER, an instruction-tuning
approach for zero-shot NER leveraging prompts enriched with definition and
guidelines. Comparisons with other state-of-the-art models, demonstrate the
superiority of SLIMER-IT on never-seen-before entity tags.
An evaluation framework and instruction-tuned model for zero-shot named entity recognition in Italian outperform existing state-of-the-art models on unseen entity types.
Named Entity Recognition BIO sequence labeling Large Language Models Zero-Shot NER SLIMER instruction-tuning prompts evaluation framework