Papers by Hoang-Quynh Le
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction (N19-1)
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| Challenge: | Existing approaches to extract relationship between entities in sentences suffer from missing or redundant information. |
| Approach: | They propose a deep neural model that combines the advantages of the two approaches to extract the relationship between two entities in a sentence. |
| Outcome: | The proposed model outperforms baseline models on the SemEval-2010 dataset. |
Large-scale Exploration of Neural Relation Classification Architectures (D18-1)
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| Challenge: | Existing studies on relation classification have been limited to a very narrow range of datasets, making comparisons between approaches difficult. |
| Approach: | They propose a multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and provides direct insights into the challenges faced by language models on relation classification. |
Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs (2025.findings-emnlp)
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| Challenge: | Experimental results demonstrate that our model outperforms existing approaches for summarizing documents. |
| Approach: | proposed model constructs a heterogeneous graph to represent a document and its relevant external citations. |
| Outcome: | The proposed model outperforms existing models in three different scenarios. |
HOPE: Hybrid Optimized Parallel Encoding with Supervised and Unsupervised Semantic Fusion for Depression Symptom Detection (2026.acl-long)
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| Challenge: | HOPE is a framework for detecting depression symptoms from social media data . it combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering . |
| Approach: | They propose a Hybrid Optimized Parallel Encoding framework that combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering. |
| Outcome: | The proposed framework outperforms existing methods on multiple benchmark datasets and shows that it can detect fine-grained symptoms and early warning of mental health risk. |