Papers by Key-Sun Choi
Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision (L18-1)
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| Challenge: | Existing approaches to relation extraction (RE) only extract relations from sentences that contain two target entities. |
| Approach: | They propose to incorporate global contexts from paragraph-into-sentence embedding into RE . they propose to use a knowledge base to extract relations between pairs of entities . |
| Outcome: | The proposed approach can learn an exact RE from sentences without syntactic parsing. |
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 . |
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%. |
Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs (C18-2)
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| Challenge: | Existing studies on relation extraction only take into account intrasentence relationships that contain pairs of entities. |
| Approach: | They propose to capture omitted arguments in relation extraction given a proper knowledge base for entities of interest. |
| Outcome: | The proposed method improves relation extraction quality by capturing omitted arguments in sentences. |
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. |
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. |
Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (2020.lrec-1)
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| Challenge: | Using current methods, the construction of multilingual FrameNets is expensive and complex. |
| Approach: | They evaluated whether crowdsourcing approaches captured cross-cultural and cross-linguistic meanings . they found that crowd workers made intuitive choices comparable to trained FrameNet experts . |
| Outcome: | The results are now available in Korean FrameNet 1.1. |