{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:08:45Z","timestamp":1754158125092,"version":"3.41.2"},"reference-count":9,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2007,8,14]],"date-time":"2007-08-14T00:00:00Z","timestamp":1187049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,8,14]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>Most e\u2010commerce web sites use HTML forms for user authentication, new user registration, newsletter subscription, and searching for products and services. The purpose of this paper is to present a method for automated classification of HTML forms, which is important for search engine applications, e.g. Yahoo Shopping and Google's Froogle, as they can be used to improve the quality of the index and accuracy of search results.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>Describes a technique for classifying HTML forms based on their features. Develops algorithms for automatic feature generation of HTML forms and a neural network to classify them.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The authors tested their classifier on an e\u2010commerce data set and a randomly retrieved data set and achieved accuracy of 94.7 and 93.9 per cent respectively. Experimental results show that the classifier is effective and efficient on both test beds, suggesting that it is a promising general purpose method.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper is of value to those involved with information management and e\u2010commerce.<\/jats:p><\/jats:sec>","DOI":"10.1108\/14684520710780412","type":"journal-article","created":{"date-parts":[[2007,8,18]],"date-time":"2007-08-18T07:01:57Z","timestamp":1187420517000},"page":"451-466","source":"Crossref","is-referenced-by-count":2,"title":["Automated classification of HTML forms on e\u2010commerce web sites"],"prefix":"10.1108","volume":"31","author":[{"given":"Yanbo","family":"Ru","sequence":"first","affiliation":[]},{"given":"Ellis","family":"Horowitz","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022031520004104200_b1","unstructured":"Cope, J., Craswell, N. and Hawking, D. 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(2000), Automatic Classification of Text Databases through Query Probing, WebDB2000, Dallas, TX.","DOI":"10.1007\/3-540-45271-0_16"},{"key":"key2022031520004104200_b5","doi-asserted-by":"crossref","unstructured":"Kwon, O. and Lee, J. (2000), \u201cWeb page classification based on k\u2010nearest neighbor approach\u201d, Proceedings of the 5th International Workshop on Information Retrieval with Asian Languages, November, pp. 9\u201015.","DOI":"10.1145\/355214.355216"},{"key":"key2022031520004104200_b6","unstructured":"Mitchell, T. (1997), Machine Learning, McGraw\u2010Hill, New York, NY, pp. 102\u20105."},{"key":"key2022031520004104200_b7","doi-asserted-by":"crossref","unstructured":"Peng, Q., Meng, W., He, H. and Yu, C. 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