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Connective Comprehension: An individual differences study ...
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Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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Re-Evaluating Early Memorization of the Qurʾān in Medieval Muslim Cultures
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In: Religions; Volume 13; Issue 2; Pages: 179 (2022)
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Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4670 (2022)
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A Deep Fusion Matching Network Semantic Reasoning Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3416 (2022)
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Abstract:
As the vital technology of natural language understanding, sentence representation reasoning technology mainly focuses on sentence representation methods and reasoning models. Although the performance has been improved, there are still some problems, such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. Given the reasoning model’s lack of reasoning depth and interpretability, a deep fusion matching network is designed in this paper, which mainly includes a coding layer, matching layer, dependency convolution layer, information aggregation layer, and inference prediction layer. Based on a deep matching network, the matching layer is improved. Furthermore, the heuristic matching algorithm replaces the bidirectional long-short memory neural network to simplify the interactive fusion. As a result, it improves the reasoning depth and reduces the complexity of the model; the dependency convolution layer uses the tree-type convolution network to extract the sentence structure information along with the sentence dependency tree structure, which improves the interpretability of the reasoning process. Finally, the performance of the model is verified on several datasets. The results show that the reasoning effect of the model is better than that of the shallow reasoning model, and the accuracy rate on the SNLI test set reaches 89.0%. At the same time, the semantic correlation analysis results show that the dependency convolution layer is beneficial in improving the interpretability of the reasoning process.
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Keyword:
attention mechanism; deep fusion matching network; long-short memory network; semantic reasoning; sentence representation
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URL: https://doi.org/10.3390/app12073416
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OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes
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In: Electronics; Volume 11; Issue 7; Pages: 1143 (2022)
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Is Early Bilingual Experience Associated with Greater Fluid Intelligence in Adults?
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In: Languages; Volume 7; Issue 2; Pages: 100 (2022)
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Design of an Artificial Intelligence of Things Based Indoor Planting Model for Mentha Spicata
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In: Processes; Volume 10; Issue 1; Pages: 116 (2022)
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Formalization of AMR Inference via Hybrid Logic Tableaux ...
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Child Social Understanding: How Theory of Mind Development is Influenced by Socio-Cultural Factors
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Neural-based Knowledge Transfer in Natural Language Processing
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Ranking Semantics for Argumentation Systems With Necessities
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In: IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03002056 ; IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, Jan 2021, Yokohama / Virtual, Japan. pp.1912-1918, ⟨10.24963/ijcai.2020/265⟩ (2021)
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Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation
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In: The Semantic Web: ESWC 2021 Satellite Events ; https://hal.inrae.fr/hal-03408000 ; Ruben Verborgh; Anastasia Dimou; Aidan Hogan; Claudia d'Amato; Ilaria Tiddi; Arne Bröring; Simon Mayer; Femke Ongenae; Riccardo Tommasini; Mehwish Alam. The Semantic Web: ESWC 2021 Satellite Events, 12739, Springer International Publishing, pp.176-185, 2021, Lecture Notes in Computer Science, 978-3-030-80417-6. ⟨10.1007/978-3-030-80418-3_30⟩ (2021)
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Multimodal Conversation Modeling via Neural Perception, Structure Learning, and Communication
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How Do Language Intensity and Artificial Intelligence (AI) Affect Perceptions of Fact-checking Messages and Evaluations of Fact-checking Agencies?
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Xue, Haoning. - : eScholarship, University of California, 2021
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