JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Na?e Bayes, and Artificial Neural Networks |
| 영문제목(English Title) |
Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Na?e Bayes, and Artificial Neural Networks |
| 저자(Author) |
Kamal Sarkar
Mita Nasipuri
Suranjan Ghose
|
| 원문수록처(Citation) |
VOL 08 NO. 04 PP. 0693 ~ 0712 (2012. 12) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Na?e Bayes, and Artificial Neural Networks for keyphrase extraction. We consider keyphrases as being phrases that consist of one or more words and as representing the important concepts in a text document. The three machine learning based keyphrase extraction methods that we use for experimentation have been compared with a publicly available keyphrase extraction system called KEA. The experimental results show that the Neural Network based keyphrase extraction method outperforms two other keyphrase extraction methods that use the Decision Tree and Na?e Bayes. The results also show that the Neural Network based method performs better than KEA. |
| 키워드(Keyword) |
Keyphrase Extraction
Decision Tree
Na?e Bayes
Artificial Neural Networks
Machine Learning
WEKA
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| 파일첨부 |
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