Papers by Van-Thuy Phi
Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction (P18-2)
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| Challenge: | et al., 1998: bootstrapping for relation extraction uses minimally supervised methods . etudes show that proposed methods for automatic seed selection and noise reduction are better than baseline systems . |
| Approach: | They propose automatic seed selection and noise reduction for distantly supervised relation extraction tasks. |
| Outcome: | The proposed methods achieve better performance than baseline systems in both tasks. |
PolyMinder: A Support System for Entity Annotation and Relation Extraction in Polymer Science Documents (2025.coling-demos)
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| Challenge: | Automated Named Entity Recognition (NER) and Relation Extraction (RE) models are tailored to the polymer domain. |
| Approach: | They propose to automate the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations. |
| Outcome: | The proposed system streamlines the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations. |
A Unified Framework for N-ary Property Information Extraction in Materials Science (2025.findings-emnlp)
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| Challenge: | a framework for extracting n-ary property information from materials science literature is proposed . the framework addresses the critical challenge of capturing complex relationships that span multiple sentences. |
| Approach: | They propose a framework for extracting n-ary property information from materials science literature . they propose three complementary approaches to capture complex relationships that span multiple sentences . |
| Outcome: | The proposed framework outperforms existing methods in n-ary property extraction tasks. |
PolyNERE: A Novel Ontology and Corpus for Named Entity Recognition and Relation Extraction in Polymer Science Domain (2024.lrec-main)
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| Challenge: | a new ontology for polymer-relevant entities and relations is available for training data . the ontologies are customizable to adapt to specific research needs. |
| Approach: | They propose a polymer-relevant ontology featuring crucial entities and relations . the ontologies are customizable to adapt to specific research needs . |
| Outcome: | The proposed ontology can extract polymer-relevant information from scientific papers . it can be customized to adapt to specific research needs . |
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN (N19-1)
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| Challenge: | Recent work on relation classification has gained much success by exploiting deep neural networks. |
| Approach: | They propose a relation classification model using Segment-level Attention-based Convolutional Neural Networks and Dependency-based Recurrent Neural networks. |
| Outcome: | The proposed model is comparable to the state-of-the-art without external lexical features on the SemEval-2010 dataset. |