Giuseppe Giacalone

Document classification is helpful for law professionals to improve content browsing and retrieval. Pretrained Language Models, such as BERT, have become established for legal document classification. However, legal content is quite diversified. For example, documents vary in length from very short maxims to relatively long judgements; certain document types are rich of domain-specific expressions and can be annotated with multiple labels from domain-specific taxonomies. This paper studies to… Visualizza altro

Document classification is helpful for law professionals to improve content browsing and retrieval. Pretrained Language Models, such as BERT, have become established for legal document classification. However, legal content is quite diversified. For example, documents vary in length from very short maxims to relatively long judgements; certain document types are rich of domain-specific expressions and can be annotated with multiple labels from domain-specific taxonomies. This paper studies to what extent existing pretrained models are suited to the legal domain. Specifically, we examine a real business case focused on Italian legal document classification. On a proprietary dataset with thousands of diversified categories (e.g., legal judgements, maxims, and legal news) we explore the use of Pretrained Language Models adapted to handle various content types.We collect both quantitative and qualitative results, highlighting best and worst cases, anomalous categories, and limitations of currently available models. Meno dettagli

Exploring legal documents such as laws, judgments, and contracts is known to be a time-consuming task. To support domain experts in efficiently browsing their contents, legal documents in electronic form are commonly enriched with semantic annotations. They consist of a list of headwords indicating the main topics. Annotations are commonly organized in taxonomies, which comprise both a set of is-a hierarchies, expressing parent/child-sibling relationships, and more arbitrary related-to semantic… Visualizza altro

Exploring legal documents such as laws, judgments, and contracts is known to be a time-consuming task. To support domain experts in efficiently browsing their contents, legal documents in electronic form are commonly enriched with semantic annotations. They consist of a list of headwords indicating the main topics. Annotations are commonly organized in taxonomies, which comprise both a set of is-a hierarchies, expressing parent/child-sibling relationships, and more arbitrary related-to semantic links. This paper addresses the use of Deep Learning-based Natural Language Processing techniques to automatically extract unknown taxonomy relationships between pairs of legal documents. Exploring the document content is particularly useful for automatically classifying legal document pairs when topic-level relationships are partly out-of-date or missing, which is quite common for related-to links. The experimental results, collected on a real heterogeneous collection of Italian legal documents, show that word-level vector representations of text are particularly effective in leveraging the presence of domain-specific terms for classification and overcome the limitations of contextualized embeddings when there is a lack of annotated data. Meno dettagli

Processing case-law contents for electronic publishing purposes is a time-consuming activity that encompasses several sub-tasks and usually involves adding annotations to the original text. On the other hand, recent trends in Artificial Intelligence and Natural Language Processing enable the automatic and efficient analysis of big textual data. In this paper we present our Machine Learning solution to three specific business problems, regularly met by a real world Italian publisher in their… Visualizza altro

Processing case-law contents for electronic publishing purposes is a time-consuming activity that encompasses several sub-tasks and usually involves adding annotations to the original text. On the other hand, recent trends in Artificial Intelligence and Natural Language Processing enable the automatic and efficient analysis of big textual data. In this paper we present our Machine Learning solution to three specific business problems, regularly met by a real world Italian publisher in their day-to-day work: recognition of legal references in text spans, new content ranking by relevance, and text classification according to a given tree of topics. Different approaches based on BERT language model were experimented with, together with alternatives, typically based on Bag-of-Words. The optimal solution, deployed in a controlled production environment, was in two out of three cases based on fine-tuned BERT (for the extraction of legal references and text classification), while, in the case of relevance ranking, a Random Forest model, with hand-crafted features, was preferred. We will conclude by discussing the concrete impact, as perceived by the publisher, of the developed prototypes. Meno dettagli

A novel hand-held drilling tool devoted to orthopedic surgery is presented in this paper. The starting point of the study is the conjecture that the invasiveness of interventions might be reduced by adding sensing, reasoning, and control capabilities to existing tools, in order to obtain controlled penetration in the patient's body and automatic discrimination among layers of different tissues. Due to the particular environment in which the tool is to work, the requirements in terms of human… Visualizza altro

A novel hand-held drilling tool devoted to orthopedic surgery is presented in this paper. The starting point of the study is the conjecture that the invasiveness of interventions might be reduced by adding sensing, reasoning, and control capabilities to existing tools, in order to obtain controlled penetration in the patient's body and automatic discrimination among layers of different tissues. Due to the particular environment in which the tool is to work, the requirements in terms of human friendliness and safety impose a careful design of the human-machine interface. The proposed approach to the development of a mechatronic tool for surgery is discussed, with emphasis on the functionality and performance of the device and the limited-usage expertise required. New applications for the proposed concepts in nonsurgical environments requiring hand-held tools are foreseen. Meno dettagli