Titlebook: Logic-Based Program Synthesis and Transformation; 18th International S Michael Hanus Conference proceedings 2009 Springer-Verlag Berlin Hei

oracle

On Negative Unfolding in the Answer Set Semantics,ng in terms of nested expressions by Lifschitz et al., and regard it as a combination of the replacement of a literal by its definition (called “pre-negative unfolding”) and double negation elimination. We give sufficient conditions for preserving the answer set semantics. We then consider a framewo

CRUDE

譏笑

Cristiano Calcagno,Dino Distefano,Peter O’Hearn,Hongseok Yangr, instance construction is very time-consuming and laborious, and it is a big challenge for natural language processing (NLP) tasks in many fields. For example, the instances of the question matching dataset CHIP in the medical field are only 2.7% of the general field dataset LCQMC, and its perform

我邪惡

Elvira Albert,Miguel Gómez-Zamalloa,Germán Pueblah. At present, there is a lack of research content for Chinese and Korean bilingualism in the field of knowledge graphs. At the same time, mainstream entity alignment methods are susceptible to the impact of data set size and graph structure heterogeneity. This paper proposes a cross-language entity

MELD

María Alpuente,Santiago Escobar,José Meseguer,Pedro Ojedacient in practice, current research focuses on designing parallel reasoning algorithms or employing high-performance computing architectures, like neural networks. No matter what architecture we choose, the computational complexity of reasoning is upper-bounded by the .-completeness or higher ones t

Presbyopia

Gustavo Arroyo,J. Guadalupe Ramos,Salvador Tamarit,Germán Vidalcient in practice, current research focuses on designing parallel reasoning algorithms or employing high-performance computing architectures, like neural networks. No matter what architecture we choose, the computational complexity of reasoning is upper-bounded by the .-completeness or higher ones t

不可救藥

Gourinath Banda,John P. Gallagherhe document level. Studies have shown that the Transformer architecture models long-distance dependencies without regard to the syntax-level dependencies between tokens in the sequence, which hinders its ability to model long-range dependencies. Furthermore, the global information among relational t

Bumptious

Pedagogy

Emanuel KitzelmannLP). Most of existing relation extraction models use convolutional or recurrent neural network and fail to capture the in-depth semantic features from the entities. These models also only focus on the training data and ignore external knowledge. In this paper, we propose a relation extraction model

Panacea