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인코더-디코더 모델에서 다양한 한국어 표현방법의 결합을 통한 언어모델링의 성능향상
- Alternative Title
- Jaeyeon Lee
- Author(s)
- 이재연
- Alternative Author(s)
- Jaeyeon Lee
- Advisor
- 손경아
- Department
- 일반대학원 컴퓨터공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2017-02
- Language
- eng
- Keyword
- Deep Learning; NLP; Korean Language Model; Sequence-to-Sequence; Encoder-Decoder▲》 [856][4 ]
- Alternative Abstract
- Most successful neural language modeling have mainly focused on English language and operated at the level of words. Although word-level based neural language model performs well, some other languages cannot get word-level token easily especially in Korean language model. In the field of natural language processing for the Korean language, morpheme-level approaches are commonly employed as alternatives to the word in English. However, it causes the dependency to the external morphology analyzer. Accordingly, character-level approaches are preferred for Korean neural language modeling, but there are several ways to represent the Korean language in character-level, as which we call them Korean-letter level and Korean-grapheme level representations. In this thesis, we investigate the best representations of the Korean language with evaluation on three experiments: reconstruction, marker classification, and spelling correction. Since there are no public datasets in the Korean language for the three tasks, we first generate the datasets using a Korean public corpus. Furthermore, we propose an advanced architecture which can effectively employ various representations for sequence-to-sequence problem in the Korean language. In our experiments, we showed that the proposed architecture outperforms the traditional architectures that use only one of the representations.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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