Using Machine Learning for Automatic Estimation of Emphases in Japanese Documents

Masaki MURATA  Yuki ABE  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.10   pp.2669-2672
Publication Date: 2017/10/01
Publicized: 2017/07/21
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8247
Type of Manuscript: LETTER
Category: Natural Language Processing
machine learning,  automatic estimation,  emphasis,  bold,  conditional random fields,  

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We propose a method for automatic emphasis estimation using conditional random fields. In our experiments, the value of F-measure obtained using our proposed method (0.31) was higher than that obtained using a random emphasis method (0.20), a method using TF-IDF (0.21), and a method based on LexRank (0.26). On the contrary, the value of F-measure of obtained using our proposed method (0.28) was slightly worse as compared with that obtained using manual estimation (0.26-0.40, with an average of 0.35).