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| 61 |
The Competitive Advantage of the Indian and Korean Film Industries: An Empirical Analysis Using Natural Language Processing Methods
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4592 (2022)
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| 62 |
Information Processing by Selective Machines
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In: Proceedings; Volume 81; Issue 1; Pages: 122 (2022)
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| 63 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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| 64 |
Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5
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In: Mathematics; Volume 10; Issue 5; Pages: 746 (2022)
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| 65 |
Measuring Gender Bias in Contextualized Embeddings
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In: Computer Sciences & Mathematics Forum; Volume 3; Issue 1; Pages: 3 (2022)
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| 66 |
Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4578 (2022)
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| 67 |
AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5
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In: Applied Sciences; Volume 12; Issue 4; Pages: 1881 (2022)
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| 68 |
An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 8 (2022)
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| 69 |
Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction
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In: Mathematics; Volume 10; Issue 8; Pages: 1344 (2022)
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Abstract:
Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. This results in low-accuracy relation extractions. To address this problem, we propose a contextual semantic-guided entity-centric graph convolutional network (CEGCN) model that enables entity mentions to obtain semantic-guided contextual information for more accurate relational representations. This model develops a self-attention enhanced neural network to concentrate on the importance and relevance of different words to obtain semantic-guided contextual information. Then, we employ a dependency tree with entities as global nodes and add virtual edges to construct an entity-centric logical adjacency matrix (ELAM). This matrix can enable entities to aggregate the semantic-guided contextual information with a one-layer GCN calculation. The experimental results on the TACRED and SemEval-2010 Task 8 datasets show that our model can efficiently use semantic-guided contextual information to enrich semantic entity representations and outperform previous models.
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Keyword:
graph convolutional network; machine learning; natural language processing; relation extraction
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URL: https://doi.org/10.3390/math10081344
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| 70 |
MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition
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In: Metabolites; Volume 12; Issue 4; Pages: 276 (2022)
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| 71 |
Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 10 (2022)
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| 72 |
X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4502 (2022)
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| 73 |
Evaluation of Chinese Natural Language Processing System Based on Metamorphic Testing
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In: Mathematics; Volume 10; Issue 8; Pages: 1276 (2022)
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| 74 |
Retrieval-Based Transformer Pseudocode Generation
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In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022)
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| 75 |
An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19
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In: Information; Volume 13; Issue 3; Pages: 137 (2022)
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| 76 |
Data of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021 ...
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| 77 |
Data of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021 ...
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| 78 |
Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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| 79 |
Scripted-sentence learning in Spanish speakers (Quique et al., 2022) ...
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| 80 |
Scripted-sentence learning in Spanish speakers (Quique et al., 2022) ...
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