Papers by Eun-kyung Kim

4 papers
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 .
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.
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020.lrec-1)

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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.

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