Complex structured documents can be intentionally represented as a tree structure decorated with attributes. Ignoring attributes (these are related to semantic aspects that can be treated separately from purely struct...Complex structured documents can be intentionally represented as a tree structure decorated with attributes. Ignoring attributes (these are related to semantic aspects that can be treated separately from purely structural aspects which interest us here), in the context of a cooperative edition, legal structures are characterized by a document model (an abstract grammar) and each intentional representation can be manipulated independently and eventually asynchronously by several co-authors through various editing tools that operate on its “partial replicas”. For unsynchronized edition of a partial replica, considered co-author must have a syntactic document local model that constraints him to ensure minimum consistency of local representation that handles with respect to the global model. This consistency is synonymous with the existence of one or more (global) intentional representations towards the global model, assuming the current local representation as her/their partial replica. The purpose of this paper is to present the grammatical structures which are grammars that permit not only to specify a (global) model for documents published in a cooperative manner, but also to derive automatically via a so call projection operation, consistent (local) models for each co-authors involved in the cooperative edition. We also show some properties that meet these grammatical structures.展开更多
The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same ...The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian <i>M<sup>L×K</sup>/M<sup>L×K</sup>/</i>1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. <span style="white-space:normal;"><i></i></span><i>In fine<span style="white-space:normal;"></span></i>, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.展开更多
This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prom...This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.展开更多
文摘Complex structured documents can be intentionally represented as a tree structure decorated with attributes. Ignoring attributes (these are related to semantic aspects that can be treated separately from purely structural aspects which interest us here), in the context of a cooperative edition, legal structures are characterized by a document model (an abstract grammar) and each intentional representation can be manipulated independently and eventually asynchronously by several co-authors through various editing tools that operate on its “partial replicas”. For unsynchronized edition of a partial replica, considered co-author must have a syntactic document local model that constraints him to ensure minimum consistency of local representation that handles with respect to the global model. This consistency is synonymous with the existence of one or more (global) intentional representations towards the global model, assuming the current local representation as her/their partial replica. The purpose of this paper is to present the grammatical structures which are grammars that permit not only to specify a (global) model for documents published in a cooperative manner, but also to derive automatically via a so call projection operation, consistent (local) models for each co-authors involved in the cooperative edition. We also show some properties that meet these grammatical structures.
文摘The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian <i>M<sup>L×K</sup>/M<sup>L×K</sup>/</i>1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. <span style="white-space:normal;"><i></i></span><i>In fine<span style="white-space:normal;"></span></i>, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.
文摘This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.
