Automatic acquisition for sensibility knowledge using co-occurrence relation

被引：6
|
作者：
Yoshinari, Tomoko [1 ]
Atlam, El-Sayed [1 ]
Morita, Kazuhiro [1 ]
Kiyoi, Kumiko [1 ]
Aoe, Jun-ichi [1 ]
机构：
[1] Univ Tokushima, Dept Informat Sci & Intelligent Syst, 2-1 Minami Josanjima, Tokushima, Tokushima 7708506, Japan
来源：
INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY | 2008年 / 33卷 / 2-3期
关键词：
sensibility expression; co-occurrence knowledge; automatic acquisition; similar expression;
D O I：
10.1504/IJCAT.2008.021944
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
When companies obtain customer opinions and requests from free-styled writings, sensibility expressions are important because they include personal subjectivity such as claims 'Too much dust'. Sensibility knowledge registered in the expressions is difficult to build manually. In order to reduce personal burden for building sensibility knowledge, this paper presents a method to acquire sensibility expressions automatically by using co-occurrence relation. According to experimental results of co-occurrence knowledge registered 1,300,000 terms, the rate of correct answers included in acquired sensibility expressions is 73.5%.
引用
收藏
页码：218 / 225
页数：8
相关论文
共 50 条
  • [1] Automatic visual inspection using the co-occurrence approach
    Tobias, OJ
    Seara, R
    Soares, FAP
    Bermudez, JCM
    [J]. 38TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, PROCEEDINGS, VOLS 1 AND 2, 1996, : 154 - 157
  • [2] Event Relation Acquisition Using Dependency Patterns and Confidence-Weighted Co-occurrence Statistics
    Higashiyama, Shohei
    Sadamasa, Kunihiko
    Onishi, Takashi
    Watanabe, Yotaro
    [J]. PROCEEDINGS OF THE 2017 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2017, : 339 - 345
  • [3] Semantic Relation Discovery by Using Co-occurrence Information
    Schulz, Stefan
    Costa, Catalina Martinez
    Kreuzthaler, Markus
    Minarro-Gimenez, Jose Antonio
    Andersen, Ulrich
    Jensen, Anders Boeck
    Maegaard, Bente
    [J]. LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2014,
  • [4] Automatic Annotation of Co-Occurrence Relations
    Goldhahn, Dirk
    Quasthoff, Uwe
    [J]. LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2010, : 3319 - 3323
  • [5] Sensibility Estimation Method for Youth Slang by Using Sensibility Co-occurrence Feature Vector Obtained from Microblog
    Matsumoto, Kazuyuki
    Yoshida, Minoru
    Kita, Kenji
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2015, : 473 - 478
  • [6] Automatic segmentation and classification using a co-occurrence based approach
    Haddon, JF
    Schneebeli, M
    Buser, O
    [J]. IMAGING TECHNOLOGIES: TECHNIQUES AND APPLICATIONS IN CIVIL ENGINEERING, 1998, : 175 - 184
  • [7] Using Knowledge Graphs to Explain Entity Co-occurrence in Twitter
    Wang, Yiwei
    Carman, Mark James
    Li, Yuan-Fang
    [J]. CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, : 2351 - 2354
  • [8] Automatic classification of pulmonary diseases using a structural co-occurrence matrix
    Peixoto, Solon Alves
    Reboucas Filho, Pedro P.
    Arun Kumar, N.
    de Albuquerque, Victor Hugo C.
    [J]. NEURAL COMPUTING & APPLICATIONS, 2020, 32 (15): : 10935 - 10945
  • [9] Automatic classification of pulmonary diseases using a structural co-occurrence matrix
    Solon Alves Peixoto
    Pedro P. Rebouças Filho
    N. Arun Kumar
    Victor Hugo C. de Albuquerque
    [J]. Neural Computing and Applications, 2020, 32 : 10935 - 10945
  • [10] Semantic Hashtag Relation Classification Using Co-occurrence Word Information
    Sungwon Seo
    Jong-Kook Kim
    Sung-Il Kim
    Jeewoo Kim
    Joongheon Kim
    [J]. Wireless Personal Communications, 2019, 107 : 1355 - 1365
12345