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of 5 387
pro vyhledávání: '"Maggini"'
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-o
Externí odkaz:
http://arxiv.org/abs/2409.15933
Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of h
Externí odkaz:
http://arxiv.org/abs/2408.06396
Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models have demonstrated strong generalization capabilities. Existing LLMs pri
Externí odkaz:
http://arxiv.org/abs/2407.01272
Crafting quizzes from educational content is a pivotal activity that benefits both teachers and students by reinforcing learning and evaluating understanding. In this study, we introduce a novel approach to generate quizzes from Turkish educational t
Externí odkaz:
http://arxiv.org/abs/2406.03397
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 introduced two specially created datasets: one with over 180,000 uniqu
Externí odkaz:
http://arxiv.org/abs/2405.07035
Autor:
Zugarini, Andrea, Zeinalipour, Kamyar, Kadali, Surya Sai, Maggini, Marco, Gori, Marco, Rigutini, Leonardo
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 that distinguish them from traditional crossword puzzles. Despite there
Externí odkaz:
http://arxiv.org/abs/2404.06186
Publikováno v:
Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010)
This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner
Externí odkaz:
http://arxiv.org/abs/2402.10617
Autor:
Globo, Achille, Trevisi, Antonio, Zugarini, Andrea, Rigutini, Leonardo, Maggini, Marco, Melacci, Stefano
Publikováno v:
Proceedings of The 6th International Conference on Social Networks Analysis, Management and Security (SNAMS 2019)
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 paraphrasing to make the conversation more natural, e.g., talking about a certa
Externí odkaz:
http://arxiv.org/abs/2402.10558
This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology. Leveraging cutting-edge large language models including GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system generates distincti
Externí odkaz:
http://arxiv.org/abs/2312.01339
Publikováno v:
Proceedings of The 20th International Conference on Inductive Logic Programming (ILP 2010)
In this paper we propose a general framework to integrate supervised and unsupervised examples with background knowledge expressed by a collection of first-order logic clauses into kernel machines. In particular, we consider a multi-task learning sch
Externí odkaz:
http://arxiv.org/abs/2311.03340