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숫자로 표상된 의미: 딥러닝 시대의 의미론Meaning in Numbers: Semantics in the Age of Deep Learning
- Other Titles
- Meaning in Numbers: Semantics in the Age of Deep Learning
- Authors
- 최재웅
- Issue Date
- 2018
- Publisher
- 서강대학교 언어정보연구소
- Keywords
- Deep Learning; meaning representation; word vector; word embedding; cosine similarity; semantic class; inference; 딥러닝; 의미표상; 워드벡터; 워드임베딩; 코사인 유사도; 의미 클래스; 추론
- Citation
- 언어와 정보 사회, v.34, pp.305 - 337
- Indexed
- KCI
- Journal Title
- 언어와 정보 사회
- Volume
- 34
- Start Page
- 305
- End Page
- 337
- ISSN
- 1598-1886
- Abstract
- The advent of some practical Artificial Intelligence (AI) applications and the wide availability of Deep Learning algorithms seem to have shaken most aspects of everyday life. In particular the arrival of the vector space modelling based on word embedding, and the availability of the tools like Word2vec signalled the era of high quality word vectors and literally have changed the world of Natural Language Processing. In this paper we discuss the nature of the vector space model as an alternative in linguistic semantics. We also discuss some of its characteristics and limitations, and some possible related linguistic issues based on results gained from applying Word2vec to two of the well known Korean corpora, the Sejong Semantically Annotated Corpus and part of the Trend21 corpora.
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Collections - College of Liberal Arts > Department of Linguistics > 1. Journal Articles
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