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Published Date Extraction System A semi-supervised approach of extraction

机译:发布日期提取系统一种半监督提取方法

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The need to extract a meaningful or relevant dates like published date from an unstructured document is a very vital cog in the wheel of information extraction and data mining field. The current approaches usage DOM (Document Object Model) manipulation for an HTML document or regex expression and rules from metadata which are not so accurate for different types of publication. The recent work in this area mainly focused on web pages and HTML pages with some good accuracy. Our approach took a leaf from those works for HTML, and along with that it extensively covers PDF document, Blog articles, and Websites. It supports several types of documents like News Articles, Patents, Scientific Articles/Journal in PDF format, Blogs, Websites and more. It also has the capabilities to learn over the period and feed the learnings back to the system as trained model. Our algorithm comprises of both supervised and unsupervised steps, and it uses natural language processing techniques.
机译:从非结构化文档中提取有意义或相关的日期(如发布日期)的需求是信息提取和数据挖掘领域的一个非常重要的问题。当前针对HTML文档或正则表达式的用法以及来自元数据的规则(对不同类型的出版物而言不太准确)使用DOM(文档对象模型)操作。该领域最近的工作主要集中在具有良好准确性的网页和HTML页面上。我们的方法从HTML的那些作品中吸取了教训,并广泛涵盖了PDF文档,博客文章和网站。它支持多种类型的文档,例如新闻文章,专利,PDF格式的科学文章/期刊,博客,网站等。它还具有在一段时间内学习并将学习内容作为训练模型反馈给系统的功能。我们的算法包括监督步骤和非监督步骤,并使用自然语言处理技术。

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