Learning to Filter Documents for Information Extraction Using Rapid Annotation

Carlos Alejandro Aguirre, Sneha Gullapalli, María F. De la Torre, Alice Lam, J. Weese, W. Hsu
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引用次数: 2

Abstract

Corpus-driven approaches to information extraction from documents face problems of relevance determination, namely determining which documents are of requisite type, structure, and content for a specified query and context. In this paper, we discuss the problem of learning to filter documents crawled from the web with respect to such relevance criteria, and in particular how to annotate document corpora for supervised classification learning approaches to this problem. For context, we describe a system aimed at extracting experimental data from scientific publications, with the long-term goal of extracting procedural information from relevant sections on experimental methodology. We consider motivating use cases for our learning filter, using the documents passed by the filter: marking up sections (or passages); capturing entities and relationships; and explaining to a domain expert why a document is relevant. These distinct use cases make the annotation task multi-faceted. Our approach focuses on speeding up annotation in learning to filter while minimizing loss of precision or recall on the learning task, using a reconfigurable user interface. We develop such an interface, report on its use in tandem with classification on a real extraction task, and discuss extensions of this work to visual scene filtering and annotation.
学习使用快速标注过滤文档以提取信息
从文档中提取信息的语料库驱动方法面临相关性确定的问题,即确定哪些文档具有特定查询和上下文所需的类型、结构和内容。在本文中,我们讨论了学习如何根据这些相关标准过滤从网络上抓取的文档的问题,特别是如何为这个问题的监督分类学习方法标注文档语料库。作为上下文,我们描述了一个旨在从科学出版物中提取实验数据的系统,其长期目标是从实验方法的相关章节中提取程序信息。我们考虑为我们的学习过滤器激励用例,使用过滤器传递的文档:标记部分(或段落);捕获实体和关系;向领域专家解释为什么一个文档是相关的。这些不同的用例使注释任务具有多面性。我们的方法侧重于加速学习中的标注过滤,同时最大限度地减少学习任务的精度损失或召回,使用可重构的用户界面。我们开发了这样一个接口,报告了它与分类在真实提取任务中的串联使用,并讨论了将这项工作扩展到视觉场景过滤和注释。
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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