Papers by Jiseong Kim
Automatic Wordnet Mapping: from CoreNet to Princeton WordNet (L18-1)
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| Challenge: | Existing mappings focus on identifying the semantic categories of CoreNet, but not the word senses. |
| Approach: | They propose to map the word senses of CoreNet into Princeton WordNet synsets by lexical relations by a taxonomy. |
| Outcome: | The proposed mapping bridging the gap between CoreNet and WordNet shows that the word senses of CoreNet are mapped with precision of 91.2%. |
Semi-automatic Korean FrameNet Annotation over KAIST Treebank (L18-1)
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| Challenge: | Annotating FrameNet over raw sentences is an expensive and complex task, because of which we have designed a semi-automatic annotation approach. |
| Approach: | They propose to use Korean FrameNet annotations to build a frame-semantic parser for English using full-text annotation and partially annotated exemplar sentences to train their models. |
| Outcome: | The proposed model is based on a lexical database of the Korean FrameNet, and its current scope, status, and limitations are discussed in the paper. |
Unsupervised Korean Word Sense Disambiguation using CoreNet (L18-1)
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| Challenge: | Unsupervised learning based Korean word sense disambiguation is needed to distinguish between sense candidates. |
| Approach: | They investigated unsupervised Korean word sense disambiguation using CoreNet, a Korean lexical semantic network. |
| Outcome: | The proposed method exhibited an 80.9% accuracy on the datasets constructed and proved to be effective for practical applications. |
Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (2020.coling-main)
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| Challenge: | Misinformation spreads across media, community, and knowledge graphs in the Web by human agents and information extraction algorithms. |
| Approach: | They propose a rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble. |
| Outcome: | The proposed approach outperforms the state-of-the-art unsupervised approaches by up to 0.12 AUC-ROC and even outperfies the supervised approach by up 0.05 AUC. |