@inproceedings{shiokawa-etal-2018-measuring,
    title = "Measuring Beginner Friendliness of {J}apanese Web Pages explaining Academic Concepts by Integrating Neural Image Feature and Text Features",
    author = "Shiokawa, Hayato  and
      Kawaguchi, Kota  and
      Han, Bingcai  and
      Utsuro, Takehito  and
      Kawada, Yasuhide  and
      Yoshioka, Masaharu  and
      Kando, Noriko",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3721",
    doi = "10.18653/v1/W18-3721",
    pages = "143--151",
    abstract = "Search engine is an important tool of modern academic study, but the results are lack of measurement of beginner friendliness. In order to improve the efficiency of using search engine for academic study, it is necessary to invent a technique of measuring the beginner friendliness of a Web page explaining academic concepts and to build an automatic measurement system. This paper studies how to integrate heterogeneous features such as a neural image feature generated from the image of the Web page by a variant of CNN (convolutional neural network) as well as text features extracted from the body text of the HTML file of the Web page. Integration is performed through the framework of the SVM classifier learning. Evaluation results show that heterogeneous features perform better than each individual type of features.",
}