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Joo-Young Lee

dblp:07/2631 · DBLP profile ↗
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7ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 1 first-authorComputer networks · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Question answering and dialogue systems · 77% Transfer learning and domain adaptation · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Question answering and dialogue systems
domain-specific question answering
0.112006
K-QARD: A Practical Korean Question Answering Framework for Restricted Domain · ACL 2006
Machine learning › Transfer learning and domain adaptation › cross-domain transfer
domain portability
0.012006
K-QARD: A Practical Korean Question Answering Framework for Restricted Domain · ACL 2006
YearPublicationVenuePosition
2020 Empirical evaluation across multiple GPU-accelerated DBMSes
abstract
In this paper we conduct an empirical study across modern GPU-accelerated DBMSes with TPC-H workloads. Our rigorous experiments demonstrate that the studied DBMSes appear to utilize GPU resource effectively but do not scale well with growing databases nor have full capability to process some complex analytical queries. Thus, we claim that the GPU DBMSes still need to be further engineered to achieve a better analytical performance.
Hawon Chu, Seounghyun Kim, Joo-Young Lee, Young-Kyoon Suh
DaMoN3
2011 Phrase Segmentation Model using Collocation and Translational Entropy
Hyoung-Gyu Lee, Joo-Young Lee, Min-Jeong Kim, Hae-Chang Rim, Joong-Hwi Shin, Young-Sook Hwang
MTSummit2
2008 Semantic Dependency Parsing using N-best Semantic Role Sequences and Roleset Information
Joo-Young Lee, Hancheol Cho, Hae-Chang Rim
CoNLL1
2007 Building a Large-Scale Commonsense Knowledge Base by Converting an Existing One in a Different Language
Yuchul Jung, Joo-Young Lee, Sung-Hyon Myaeng, Hae-Chang Rim
CICLing2
2006 K-QARD: A Practical Korean Question Answering Framework for Restricted Domain
abstract
We present a Korean question answering framework for restricted domains, called K-QARD. K-QARD is developed to achieve domain portability and robustness, and the framework is successfully applied to build question answering systems for several domains.
Young-In Song, Hoo-Jung Chung, Kyoung-Soo Han, Joo-Young Lee, Hae-Chang Rim, Jae-Won Lee
ACL4
2002 Design of non-blocking permutation generator
abstract
The Banyan network is a popular and basic structure of multistage ATM switches. This paper presents a novel approach to resolve the internal blocking of the banyan network by using a nonblocking permutation generator (NBPG). The NBPG performs two functions, i.e., the first function is to extract the conflict cells from the incoming cells and the second function is to re-assign new input port addresses to the conflict cells. As a result, NBPG generates nonblocking I/O permutations. To estimate the performance of NBPG, we provide several simulation results.
Joo-Young Lee, Jae-Il Jung
ICC1
2002 A human-oriented image retrieval system using interactive genetic algorithm
abstract
Content-based image retrieval has been actively studied in several fields. This provides more effective management and retrieval of images than the keyword-based approach. However, most of the conventional methods lack the capability to effectively incorporate human intuition and emotion into retrieving images. It is difficult to obtain satisfactory results when the user wants the image that cannot be explicitly described or can be requested only based on impression. In order to solve this problem and supplement the lack of the user's expression capability, we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). This system extracts the feature from images by wavelet transform, and provides a user-friendly means to retrieve an image from a large database when the user cannot clearly define what the image must be. Therefore, this facilitates the search for the image not only with explicit queries, but also with implicit queries such as "cheerful impression," "gloomy impression," and so on. A thorough experiment with a 2000 image database shows the usefulness of the proposed system.
Sung-Bae Cho, Joo-Young Lee
IEEE Trans. Syst. Man Cybern. Part A2