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Sung Dong Kim

dblp:61/5122 · DBLP profile ↗
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5ranked-venue papers
3as first author
0since 2021 · last 2006
—ORCID · none

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

Artificial intelligence and machine learning · 4 · 3 first-authorSystems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1 · 1 first-author

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
Information extraction and text analysis · 100%

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › text segmentation
sentence segmentation		 0.012000
Reducing Parsing Complexity by Intra-Sentence Segmentation based on Maximum Entropy Model · EMNLP 2000
Natural language and speech › Information extraction and text analysis
syntactic parsing		 0.012000
Reducing Parsing Complexity by Intra-Sentence Segmentation based on Maximum Entropy Model · EMNLP 2000

Methods — techniques the papers use, named apart from their topics

maximum entropy model · 0.0
YearPublicationVenuePosition
2006 CSTallocator: Call-Site Tracing Based Shared Memory Allocator for False Sharing Reduction in Page-Based DSM Systems
Jongwoo Lee, Sung Dong Kim, Jae Won Lee, Jangmin O
 HPCC2
2002 A Two-Phase Stock Trading System Using Distributional Differences
Sung Dong Kim, Jae Won Lee, Jongwoo Lee, Jinseok Chae
 DEXA1
2002 PATI: An Approach for Identifying and Resolving Ambiguities
Jae Won Lee, Sung Dong Kim
 PRICAI2
2001 Learning-based Intrasentence Segmentation for Efficient Translation of Long Sentences
Sung Dong Kim, Byoung-Tak Zhang, Yung Taek Kim
 Mach. Transl.1
2000 Reducing Parsing Complexity by Intra-Sentence Segmentation based on Maximum Entropy Model
abstract
Long sentence analysis has been a critical problem because of high complexity. This paper addresses the reduction of parsing complexity by intra-sentence segmentation, and presents maximum entropy model for determining segmentation positions. The model features lexical contexts of segmentation positions, giving a probability to each potential position. Segmentation coverage and accuracy of the proposed method are 96% and 88% respectively. The parsing efficiency is improved by 77% in time and 71% in space.
Sung Dong Kim, Byoung-Tak Zhang, Yung Tack Kim
 EMNLP1