379 research outputs found
Coptic SCRIPTORIUM: Digitizing a Corpus for Interdisciplinary Research in Ancient Egyptian
Coptic, having evolved from the language of the hieroglyphs of the pharaonic era, represents the last phase of the Egyptian language and is pivotal for a wide range of disciplines, such as linguistics, biblical studies, the history of Christianity, Egyptology, and ancient history. The Coptic language has proven essential for the decipherment and continued study of Ancient Egyptian and is of major interest for Afro-Asiatic linguistics and Coptic linguistics in its own right. Coptic manuscripts are sources for biblical and extra-biblical texts and document ancient and Christian history. Coptic SCRIPTORIUM will advance knowledge in these fields by increasing access to now largely inaccessible texts of historical, religious, and linguistic significance. The project designs digital tools and methodologies and applies them to literary texts, creating a rich open-access corpus
Lynx: A knowledge-based AI service platform for content processing, enrichment and analysis for the legal domain
The EU-funded project Lynx focuses on the creation of a knowledge graph for the legal domain (Legal Knowledge Graph, LKG) and its use for the semantic processing, analysis and enrichment of documents from the legal domain. This article describes the use cases covered in the project, the entire developed platform and the semantic analysis services that operate on the documents. © 202
Towards an interoperable ecosystem of AI and LT platforms : a roadmap for the implementation of different levels of interoperability
With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER
MetaGPT: Meta Programming for Multi-Agent Collaborative Framework
Recently, remarkable progress has been made in automated task-solving through
the use of multi-agents driven by large language models (LLMs). However,
existing works primarily focuses on simple tasks lacking exploration and
investigation in complicated tasks mainly due to the hallucination problem.
This kind of hallucination gets amplified infinitely as multiple intelligent
agents interact with each other, resulting in failures when tackling
complicated problems.Therefore, we introduce MetaGPT, an innovative framework
that infuses effective human workflows as a meta programming approach into
LLM-driven multi-agent collaboration. In particular, MetaGPT first encodes
Standardized Operating Procedures (SOPs) into prompts, fostering structured
coordination. And then, it further mandates modular outputs, bestowing agents
with domain expertise paralleling human professionals to validate outputs and
reduce compounded errors. In this way, MetaGPT leverages the assembly line work
model to assign diverse roles to various agents, thus establishing a framework
that can effectively and cohesively deconstruct complex multi-agent
collaborative problems. Our experiments conducted on collaborative software
engineering tasks illustrate MetaGPT's capability in producing comprehensive
solutions with higher coherence relative to existing conversational and
chat-based multi-agent systems. This underscores the potential of incorporating
human domain knowledge into multi-agents, thus opening up novel avenues for
grappling with intricate real-world challenges. The GitHub repository of this
project is made publicly available on: https://github.com/geekan/MetaGP
Doctor of Philosophy
dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards
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