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Learning Translation-Based Knowledge Graph Embeddings by N-Pair Translation Loss
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In: Applied Sciences ; Volume 10 ; Issue 11 (2020)
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2 |
Aspect-Based Sentiment Analysis Using Aspect Map
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In: Applied Sciences ; Volume 9 ; Issue 16 (2019)
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Abstractive Sentence Compression with Event Attention
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In: Applied Sciences ; Volume 9 ; Issue 19 (2019)
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A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects
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In: Applied Sciences ; Volume 8 ; Issue 12 (2018)
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A Sentence-To-Sentence Relation Network For Recognizing Textual Entailment ...
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A Sentence-To-Sentence Relation Network For Recognizing Textual Entailment ...
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7 |
Target Concept Selection By Property Overlap In Ontology Population ...
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Target Concept Selection By Property Overlap In Ontology Population ...
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9 |
Determining The Gender Of Korean Names For Pronoun Generation ...
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Determining The Gender Of Korean Names For Pronoun Generation ...
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11 |
Resolving Dependency Ambiguity Of Subordinate Clauses Using Support Vector Machines ...
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Resolving Dependency Ambiguity Of Subordinate Clauses Using Support Vector Machines ...
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Ontology Kernel - A Convolution Kernel for Ontology Alignment
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In: http://journal.iis.sinica.edu.tw/paper/1/130648-3.pdf?cd%3D4FDE03273B6C6DD40
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14 |
Korean Compound Noun Decomposition Using Syllabic Information Only
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In: http://scai.snu.ac.kr/~scai/Publications/Journals/International/LNCS_2945_143-154(2004).pdf
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15 |
Semantic Role Determination in Korean Relative Clauses Using Idiomatic Patterns
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In: http://nova.snu.ac.kr/~sbpark/PostScript/iccpol97.ps
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16 |
Determining the Gender of Korean Names for Pronoun Generation
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In: http://www.waset.org/journals/waset/v32/v32-9.pdf
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17 |
English-Korean Machine Transliteration by Combining Statistical Model and Web Search
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In: http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp15-20.pdf
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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.
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Keyword:
Index Terms—Machine transliteration
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URL: http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp15-20.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.3244
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