Earticle

초록

영어

One of the most important processesin a machine learning-based natural language processing module is to represent words by inputting the module. This can be accomplished by representing words in one-hot form with a large vector size without applying the concept of semantic similarity between words, or by word representation (word embedding) with vectors to represent lexical similarity. This has attracted keen research interest by improving the performance of several natural language processing modelssuch as syntactic parsing and sentiment analysis (also known as opinion mining). In this study, classification performance of Word2Vec, canonical correlation analysis (CCA), and GloVeare tested on a corpus that established using the titles and abstractsof 204,674biomedical articles published in PubMed. Categories include disease name, disease symptom, and ovarian cancer marker.Ovarian cancer markers were used as bio-markers.The classification performance of each word representation model for each category is visualized by mapping the results in two-dimensional word representations using t-distributed stochastic neighbor embedding (t-SNE).

목차

Abstract
 1. Introduction
 2. Word Representation
  2.1. Word2Vec
  2.2. CCA
  2.3. GloVe
 3. Data
 4. Test
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Young-Shin Youn [ Department of Convergence Software, Hallym University, Korea, Bio-IT Research Center, Hallym University, Korea ]
• Kyung-Min Nam [ Department of Convergence Software, Hallym University, Korea, Bio-IT Research Center, Hallym University, Korea ]
• Hye-Jeong Song [ Department of Convergence Software, Hallym University, Korea, Bio-IT Research Center, Hallym University, Korea ]
• Jong-Dae Kim [ Department of Convergence Software, Hallym University, Korea, Bio-IT Research Center, Hallym University, Korea ]
• Chan-Young Par [ Department of Convergence Software, Hallym University, Korea, Bio-IT Research Center, Hallym University, Korea ]
• Yu-Seop Kim [ Department of Convergence Software, Hallym University, Korea, Bio-IT Research Center, Hallym University, Korea ] corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605