Titlebook: Masters Theses in the Pure and Applied Sciences; Accepted by Colleges Wade H. Shafer Book 1986 Plenum Press, New York 1986 astronomy.calcul

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nautical

Wade H. Shafer and LLM solver to improve their performance. For the tree-based solver, we propose an ensemble learning framework based on ten-fold cross-validation and voting mechanism. In the LLM solver, we adopt self-consistency (SC) method to improve answer selection. Experimental results demonstrate the effec

uncertain

Wade H. Shafer and LLM solver to improve their performance. For the tree-based solver, we propose an ensemble learning framework based on ten-fold cross-validation and voting mechanism. In the LLM solver, we adopt self-consistency (SC) method to improve answer selection. Experimental results demonstrate the effec

malapropism

HARP

Wade H. Shafertail about how consistent solutions training affects the work process of beam search. In addition, we found significant differences between models trained using consistent solutions and those trained without consistent solutions, so the model ensemble technique is applied to improve model performanc

Inoperable

Wade H. Shaferstopwords removing of TCM cases, which improved 0.087 compared with no TCM stopwords removing. This paper introduces natural language processing into the TCM auxiliary diagnosis problem, in order to improve the informationization, standardization and intelligence of TCM in the new era.

RODE

Wade H. Shafere-of-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure for better representation of dependency relations in tree kernel-based methods. To our knowledge, this is the first research of tree kernel-based SRL on effectively exploring dependency relationship informatio

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