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Seung-hoon Na (8)
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Heeseung Jung; Kangil Kim; Jong-Hun Shin; Seung-Hoon Na; Sangkeun Jung; Sangmin Woo (1)
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Simple Search
Hits 1 – 9 of 9
1
Impact of Sentence Representation Matching in Neural Machine Translation
Heeseung Jung; Kangil Kim; Jong-Hun Shin; Seung-Hoon Na; Sangkeun Jung; Sangmin Woo
In: Applied Sciences; Volume 12; Issue 3; Pages: 1313 (2022)
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2
A 2-poisson model for probabilistic coreference of named entities for improved text retrieval
Seung-hoon Na
;
Hwee Tou Ng
In: http://www.comp.nus.edu.sg/~nght/pubs/sigir09.pdf (2009)
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3
Collins-LA: Collins’ Head-Driven Model with Latent Annotation
Seung-hoon Na
;
Meixun Jin
;
In-su Kang
...
In: http://www.gelbukh.com/cicling/2008/rcs-vol-33/04-na.pdf (2008)
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4
POSTECH at NTCIR-6: Combining evidences of multiple term extractions for mono-lingual and cross-lingual retrieval in Korean and Japanese
Seung-hoon Na
;
Jungi Kim
;
Ye-ha Lee
...
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/27-revised-20070522.pdf (2007)
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5
Conceptual Schema Approach to Natural Language Database Access
In-su Kang
;
Seung-hoon Na
;
Jong-hyeok Lee
In: http://www.alta.asn.au/events/altss_w2003_proc/altw/papers/kang-final.pdf
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6
Term Extractions for Mono-lingual and Cross-lingual Retrieval in Korean and Japanese
Seung-hoon Na
;
Jungi Kim
;
Ye-ha Lee
;
Jong-hyeok Lee
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/27.pdf
Abstract:
This paper describes our methodologies for NTCIR-6 CLIR involving Korean and Japanese, and reports the official result for Stage 1 and Stage 2. We participated in three tracks: K-K and J-J monolingual tracks and J-K cross-lingual tracks. As in the previous year, we focus on handling segmentation ambiguities in Asian languages. As a result, we prepared multiple term representations for documents and queries, of which ranked results are merged to generate final ranking. From official results, our methodology in Korean won the top in 6 subtasks of total 9 subtasks for Stage 2,and won the top in 2 subtasks of total 3 subtasks for Stage 1. Even though our system is the same as the previous one, final performances from NTCIR-3 to NTCIR-5 are further improved over our previous results by slightly modifying the feedback parameters.
Keyword:
Crosslingual Information Retrieval
;
Information Retrieval
;
Language Modeling Approach Hangul character not Kanji
;
Multiple Evidence Combination
;
Probabilistic Retrieval Model
;
Query Translation
;
the number of different
;
Unsupervised Segmentation
URL:
http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/27.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.9770
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7
Enriching Document Representation via Translation for Improved Monolingual Information Retrieval
Seung-hoon Na
;
Hwee Tou Ng
In: http://www.comp.nus.edu.sg/~nght/pubs/sigir11.pdf
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8
Parsimonious Translation Models for Information Retrieval ABSTRACT
Seung-hoon Na
In: http://dcs.uni-pannon.hu/CIR/cikkek/parsimonious_translation_models.pdf
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9
World Scientific Publishing Company Translation Probabilities in Cross-Language Information Retrieval
In-su Kang
;
Seung-hoon Na
;
Jong-hyeok Lee
In: http://www.mti.ugm.ac.id/~adji/courses/resources/doctor/WorldSci/2005/June/TransProbability05.pdf
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