Classification-Based Adaptive Web Scraper

S. UjwalB.V., Bharat Gaind, Abhishek Kundu, Anusha K. Holla, Mukund Rungta
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引用次数: 9

Abstract

Web scraping is an important problem in computer science. The problem with the commonly-used position or structure-based web scraping tools is that they need to be manually reconfigured as soon as the structure of the web page changes. In this paper, we try to solve this problem of information extraction for web pages consisting of repetitive blocks. We extract these blocks and their constituent attributes, using a novel classification-based approach. Our approach gives high accuracy when used to extract product-offers from an offer-aggregator website. It is also highly adaptive to the changing structure of a website.
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基于分类的自适应Web Scraper
网络抓取是计算机科学中的一个重要问题。常用的基于位置或结构的网页抓取工具的问题是,一旦网页的结构发生变化,它们就需要手动重新配置。在本文中,我们试图解决由重复块组成的网页的信息提取问题。我们使用一种新的基于分类的方法提取这些块及其组成属性。当用于从报价聚合器网站提取产品报价时,我们的方法具有很高的准确性。它还能高度适应网站结构的变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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