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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 17 of 17 1 Learning Translation-Based Knowledge Graph Embeddings by N-Pair Translation Loss Hyun-Je Song; A-Yeong Kim; Seong-Bae Park In: Applied Sciences ; Volume 10 ; Issue 11 (2020) BASE Show details 2 Aspect-Based Sentiment Analysis Using Aspect Map Yunseok Noh; Seyoung Park; Seong-Bae Park In: Applied Sciences ; Volume 9 ; Issue 16 (2019) BASE Show details 3 Abstractive Sentence Compression with Event Attention Su Jeong Choi; Ian Jung; Seyoung Park... In: Applied Sciences ; Volume 9 ; Issue 19 (2019) BASE Show details 4 A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects Seyoung Park; Seong-Bae Park; Laith H. Baniata In: Applied Sciences ; Volume 8 ; Issue 12 (2018) BASE Show details 5 A Sentence-To-Sentence Relation Network For Recognizing Textual Entailment ... Ampomah, Isaac K. E.; Seong-Bae Park; Sang-Jo Lee. - : Zenodo, 2016 BASE Show details 6 A Sentence-To-Sentence Relation Network For Recognizing Textual Entailment ... Ampomah, Isaac K. E.; Seong-Bae Park; Sang-Jo Lee. - : Zenodo, 2016 BASE Show details 7 Target Concept Selection By Property Overlap In Ontology Population ... Seong-Bae Park; Kim, Sang-Soo; Sewook Oh. - : Zenodo, 2008 BASE Show details 8 Target Concept Selection By Property Overlap In Ontology Population ... Seong-Bae Park; Kim, Sang-Soo; Sewook Oh. - : Zenodo, 2008 BASE Show details 9 Determining The Gender Of Korean Names For Pronoun Generation ... Seong-Bae Park; Hee-Geun Yoon. - : Zenodo, 2007 BASE Show details 10 Determining The Gender Of Korean Names For Pronoun Generation ... Seong-Bae Park; Hee-Geun Yoon. - : Zenodo, 2007 BASE Show details 11 Resolving Dependency Ambiguity Of Subordinate Clauses Using Support Vector Machines ... Kim, Sang-Soo; Seong-Bae Park; Sang-Jo Lee. - : Zenodo, 2007 BASE Show details 12 Resolving Dependency Ambiguity Of Subordinate Clauses Using Support Vector Machines ... Kim, Sang-Soo; Seong-Bae Park; Sang-Jo Lee. - : Zenodo, 2007 BASE Show details 13 Ontology Kernel - A Convolution Kernel for Ontology Alignment Jeong-woo Son; Hee-guen Yoon; Seong-bae Park In: http://journal.iis.sinica.edu.tw/paper/1/130648-3.pdf?cd%3D4FDE03273B6C6DD40 BASE Show details 14 Korean Compound Noun Decomposition Using Syllabic Information Only Seong-bae Park; Jeong-ho Chang; Byoung-tak Zhang In: http://scai.snu.ac.kr/~scai/Publications/Journals/International/LNCS_2945_143-154(2004).pdf BASE Show details 15 Semantic Role Determination in Korean Relative Clauses Using Idiomatic Patterns Seong-bae Park; Yung Taek Kim In: http://nova.snu.ac.kr/~sbpark/PostScript/iccpol97.ps BASE Show details 16 Determining the Gender of Korean Names for Pronoun Generation Seong-bae Park; Hee-geun Yoon In: http://www.waset.org/journals/waset/v32/v32-9.pdf BASE Show details 17 English-Korean Machine Transliteration by Combining Statistical Model and Web Search Hyun-je Song; Seong-bae Park In: http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp15-20.pdf Abstract: Abstract—Machine transliteration is an automatic method for translating source language words into phonetically equivalent target language ones. Many previous methods were devoted to translating the word that only traces phonological phenomena of the source language and the resulting showed good performance. However, there are a lot of names originated from not only the source language but also non-source languages. The existing methods fail in showing high accuracy when the names comes from the non-source language since they focus on names in source language. To deal with this problem, this paper describes a hybrid method which combines statistical model and web search for improving machine transliteration performance. The proposed method constructs a base system that stands on a statistical model to produce candidates, then expands candidates from web documents. With these candidates, it finds the most appropriate answer without any external resources. The experimental results present that the proposed method achieves higher performance than statistical model and web search respectively. Keyword: Index Terms—Machine transliteration URL: http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp15-20.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.3244 BASE Hide details

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