Papers by Dai Quoc Nguyen
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (N19-1)
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| Challenge: | Existing knowledge graphs with billions of triples are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose to embed relationship triples into a capsule network using a convolution layer and multiple filters to generate feature maps. |
| Outcome: | The proposed model outperforms strong search personalization baselines on two benchmark datasets and outperformed previous state-of-the-art models on WN18RR and FB15k-237. |
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (N18-2)
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| Challenge: | Existing knowledge base embedding models are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose a convolutional neural network embedding model for knowledge base completion that captures global relationships and transitional characteristics. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization (2020.acl-main)
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| Challenge: | Existing knowledge graph embedding methods do not allow for the prediction of new triples, such as for search personalization tasks. |
| Approach: | They propose a relational memory network to encode potential dependencies in relationship triples by a transformer self-attention mechanism. |
| Outcome: | The proposed model obtains state-of-the-art results on SEARCH17, WN11 and FB13 for the search personalization task, and on a convolutional neural network-based decoder. |
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit (N18-5)
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| Challenge: | Using word segmenters and POS taggers, Vietnamese NLP pipelines are no longer considered SOTA models for Vietnamese. |
| Approach: | They propose a Java NLP annotation pipeline for Vietnamese that provides rich linguistic annotations. |
| Outcome: | The proposed toolkit provides rich linguistic annotations to facilitate research work on Vietnamese NLP. |
A Fast and Accurate Vietnamese Word Segmenter (L18-1)
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| Challenge: | Experimental results show that our approach outperforms previous state-of-the-art approaches in terms of accuracy and performance speed. |
| Approach: | They propose a method where rules are stored in an exception structure and new rules are only added to correct segmentation errors. |
| Outcome: | The proposed approach outperforms existing methods on Vietnamese treebank benchmarks. |