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Subject Analysis Ex Machina: Developing A Subject Heading Recommendation Service For Jmu Libraries, Steven W. Holloway
Subject Analysis Ex Machina: Developing A Subject Heading Recommendation Service For Jmu Libraries, Steven W. Holloway
Libraries
Results of a 2022 evaluation of ANNIF, open-source software designed to generate controlled vocabulary subject headings, using James Madison University Libraries resources.
A Gentle Introduction To Chatgpt, Steven W. Holloway
A Gentle Introduction To Chatgpt, Steven W. Holloway
Libraries
A guest lecture on the state of commercial generative transformer technology, mid-2023, to a general audience at Staunton Public Library.
How I Read An Article That Uses Machine Learning Methods, Aziz Nazha, Olivier Elemento, Shannon Mcweeney, Moses Miles, Torsten Haferlach
How I Read An Article That Uses Machine Learning Methods, Aziz Nazha, Olivier Elemento, Shannon Mcweeney, Moses Miles, Torsten Haferlach
Kimmel Cancer Center Faculty Papers
No abstract provided.
Querying The Past: Automatic Source Attribution With Language Models, Ryan Muther, Mathew Barber, David Smith
Querying The Past: Automatic Source Attribution With Language Models, Ryan Muther, Mathew Barber, David Smith
Faculty & Staff Publications
This paper explores new methods for locating the sources used to write a text by 昀椀ne-tuning a variety of language models to rerank candidate sources. These methods promise to shed new light on traditions with complex citational practices, such as in medieval Arabic where citations are ambiguous and boundaries of quotation are poorly defined. After retrieving candidates sources using a baseline BM25 retrieval model, a variety of reranking methods are tested to see how effective they are at the task of source attribution. We conduct experiments on two datasets—English Wikipedia and medieval Arabic historical writing—and employ a variety of retrieval- …
Artificially Intelligent Computer Assisted Language Learning System With Ai Student Component, Denee M. Mcclain
Artificially Intelligent Computer Assisted Language Learning System With Ai Student Component, Denee M. Mcclain
Capstone Research Projects
Intelligent Computer Assisted Language Learning (ICALL) systems follow an accepted format, which utilizes an artificially intelligent tutor. The systems allow the user to input a sentence in the target language and the AI tutor analyzes the sentence and provides error correction. This approach can be expensive, impractical, and inflexible. Inflexibility can result in a lower quality of learning for the users of these systems. Here I present an alternative format for ICALL systems that utilizes an artificially intelligent student. This alternative is cost effective and practical because it does not require extra development time to make the artificial intelligence an …