Studies on Summarization of Multimedia Contents Based on Relational Structure between Text Tables and Images

基于文本表与图像关系结构的多媒体内容摘要研究

• 批准号: 13680452
• 负责人: ENDO Tsutomu
• 金额: $ 1.54万
• 依托单位: Kyushu Institute Of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680452/
• 关键词: multimedia document; WWW; summarization; information integration; information retrieval; information extraction; Kansei information; document generation

This research intends to develop a system which summarizes product (PCs) information retrieved from Web sites based on relational structure between text, tables and images, and presents products suitable for a user's request.1.Extraction of product specifications from HTML documents.We proposed a method for extracting specifications from HTML documents using TSVMs (Transductive Support Vector Machines). The elements of a feature vector are keywords with normalized TF-DF weighting. We achieved 95% recall with 99% precision.2.Characteristic-data extraction and support system for PC selection.The specifications written in HTML are converted into normal form called table structure. The quantitative attributes are extracted by comparing them with the mean or mode of all sample data, and the qualitative ones are extracted using knowledge provided manually. The recommended PCs are dynamically determined from the extracted data by a user's request and relevance feedback. Moreover, a radar chart and Japanese sentences are generated from specifications.3.Classification of images and feature extraction.We proposed a method for classifying the contents of images using weighted keywords extracted from their neighboring sentences. We achieved 79% accuracy by TF-IDF weighting. We also developed a system which eliminates the background from a PC image, and classify the color of PC using C4.5.

这项研究旨在开发一个系统，该系统根据文本，表和图像之间的关系结构总结了从网站检索的产品（PC）信息，并呈现适合用户请求的产品。1。html文档中产品规格的删除。我们提出了一种使用TSVM（TSVMS）（TransDuctive vector Machiness）从HTML文档中提取HTML文档的方法。特征向量的元素是具有标准化TF-DF加权的关键字。我们以99％的精度实现了95％的召回。2。用于选择PC选择的特征数据提取和支持系统。将HTML编写的规格转换为正常形式，称为表结构。通过将其与所有样本数据的平均值或模式进行比较，并使用提供的知识来提取定量属性。推荐的PC通过用户的请求和相关反馈从提取的数据中动态确定。此外，从规格中生成了雷达图和日语句子。3。图像的分类和特征提取。我们提出了一种使用从其相邻句子中提取的加权关键字对图像内容进行分类的方法。我们通过TF-IDF加权实现了79％的精度。我们还开发了一个系统，该系统可以从PC图像中消除背景，并使用C4.5对PC的颜色进行分类。

项目成果

林晃司, 嶋田和孝, 遠藤勉: "WWWからの性能表抽出のためのキーワード獲得と重み付け"電子情報通信学会技術研究報告Tl2002-48〜53[思考と言語]. Vol.102 No.688. 13-18 (2003)

Koji Hayashi、Kazutaka Shimada、Tsutomu Endo：“从 WWW 中提取性能表的关键字获取和加权”IEICE 技术研究报告 Tl2002-48~53 [Thinking and Language] Vol.102 No.688 (2003)。

福本篤史, 遠藤勉, 嶋田和孝: "Information Extraction from Specifications on the World Wide Web"Proceedings of PACLING2001 (Pacific Association for Computational Linguistics 2001). 109-116 (2001)

Atsushi Fukumoto、Tsutomu Endo、Kazutaka Shimada：“从万维网规范中提取信息”PACLING2001 论文集（太平洋计算语言学协会 2001 年）109-116 (2001)。

嶋田和孝, 遠藤勉他: "Information Modeling and Knowledge Bases XV"IOS Press. 333 (2004)

Kazutaka Shimada、Tsutomu Endo 等人：“信息建模和知识库 XV”IOS Press 333 (2004)。

遠藤勉: "対話支援型問題解決のための文脈情報処理"教育システム情報学会研究報告. Vol.18 No.3. 23-28 (2003)

Tsutomu Endo：“对话支持的问题解决的上下文信息处理”教育系统和信息研究学会的研究报告第 18 卷第 3 期（2003 年）。

Atsushi Fukumoto, Tsutomu Endo, Kazutaka Shimada: "Information Extraction from Specifications on the World Wide Web"Proceedings of PACLING2001 (Pacific Association for Computational Linguistics 2001). 109-116 (2001)

Atsushi Fukumoto、Tsutomu Endo、Kazutaka Shimada：“从万维网规范中提取信息”PACLING2001 论文集（太平洋计算语言学协会 2001 年）。