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Creators/Authors contains: "George, C."

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  1. Free, publicly-accessible full text available September 1, 2023
  2. Free, publicly-accessible full text available August 11, 2023
  3. While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible for most users. Here we attempt to combine the advantages of these two directions while mitigating their drawbacks. We adapt recent advances from the adjacent field of program synthesis to information extraction, synthesizing rules from provided examples. We use a transformer-based architecture to guide an enumerative search, and show that this reduces the number of steps that need to be explored before a rule is found. Further, we show that our synthesized rules achieve state-of-the-art performance on the 1-shot scenario of a task that focuses on few-shot learning for relation classification, and competitive performance in the 5-shot scenario.
    Free, publicly-accessible full text available June 1, 2023
  4. We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples, which are collected with a search interface. The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system. Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting. On a second evaluation on a widely-used relation extraction dataset (TACRED), our method generates rules that outperform considerably manually written patterns. Our code, demo, and documentation is available at https://clulab.github.io/odinsynth/.
    Free, publicly-accessible full text available July 1, 2023
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  6. The separation and purification of niobium and tantalum, which co-occur in natural sources, is difficult due to their similar physical and chemical properties. The current industrial method for separating Ta/Nb mixtures uses an energy-intensive process with caustic and toxic conditions. It is of interest to develop alternative, fundamental methodologies for the purification of these technologically important metals that improve upon their environmental impact. Herein, we introduce new Ta/Nb imido compounds: M( t BuN)(TriNOx) (1-M) bound by the TriNOx 3− ligand and demonstrate a fundamental, proof-of-concept Ta/Nb separation based on differences in the imido reactivities. Despite the nearly identical structures of 1-M, density functional theory (DFT)-computed electronic structures of 1-M indicate enhanced basic character of the imido group in 1-Ta as compared to 1-Nb. Accordingly, the rate of CO 2 insertion into the MN imido bond of 1-Ta to form a carbamate complex (2-Ta) was selective compared to the analogous, unobserved reaction with 1-Nb. Differences in solubility between the imido and carbamate complexes allowed for separation of the carbamate complex, and led to an efficient Ta/Nb separation ( S Ta/Nb = 404 ± 150) dependent on the kinetic differences in nucleophilicities between the imido moieties in 1-Ta and 1-Nb.
    Free, publicly-accessible full text available June 15, 2023
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