Papers by Sangha Nam
A Korean Knowledge Extraction System for Enriching a KBox (C18-2)
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| Challenge: | Existing systems for knowledge extraction from natural language sentences are lacking for all languages. |
| Approach: | They propose a Korean knowledge extraction system and web interface for enriching a KBox knowledge base based on the Korean DBpedia. |
| Outcome: | The proposed system can extract factual knowledge from natural language sentences . the endpoint can be used to add knowledge to a KBox knowledge base anytime and anywhere . |
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. |
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020.lrec-1)
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Sangha Nam, Minho Lee, Donghwan Kim, Kijong Han, Kuntae Kim, Sooji Yoon, Eun-kyung Kim, Key-Sun Choi
| Challenge: | Existing studies on information extraction from unstructured texts lack a coherent evaluation of all tasks. |
| Approach: | They propose to use crowdsourcing data to develop a Korean information extraction initiative point . they propose to train and evaluate four Korean information extracting tasks using a state-of-the-art model . |
| Outcome: | The proposed model will be used to evaluate four Korean information extraction tasks using crowdsourcing data. |