12 citations · 56 across the 16 of their papers we have counts for
9 papers · 2 filters
SLIMER-IT: Zero-Shot NER on Italian Language
Andrew Zamai, Leonardo Rigutini, Marco Maggini +1
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, the…
Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER
Andrew Zamai, Andrea Zugarini, Leonardo Rigutini +2
Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models h…
A Turkish Educational Crossword Puzzle Generator
Kamyar Zeinalipour, Yusuf Gökberk Keptiğ, Marco Maggini +2
This paper introduces the first Turkish crossword puzzle generator designed to leverage the capabilities of large language models (LLMs) for educational purposes. In this work, we…
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali +3
Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues t…
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
Stefano Melacci, Achille Globo, Leonardo Rigutini
Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent introduction of Word Embeddings…
Neural paraphrasing by automatically crawled and aligned sentence pairs
Achille Globo, Antonio Trevisi, Andrea Zugarini +3
Paraphrasing is the task of re-writing an input text using other words, without altering the meaning of the original content. Conversational systems can exploit automatic paraphras…
Fast Vocabulary Transfer for Language Model Compression
Leonidas Gee, Andrea Zugarini, Leonardo Rigutini +1
Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer.…
BUSTER: a "BUSiness Transaction Entity Recognition" dataset
Andrea Zugarini, Andrew Zamai, Marco Ernandes +1
Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reas…
Multi-word Tokenization for Sequence Compression
Leonidas Gee, Leonardo Rigutini, Marco Ernandes +1
Large Language Models have proven highly successful at modelling a variety of tasks. However, this comes at a steep computational cost that hinders wider industrial uptake. In this…