The Tradeoffs Between Open and Traditional Relation Extraction
The Tradeoffs Between Open and Traditional Relation Extraction
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发表时间：
2008-06
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通讯作者：
Michele Banko;Oren Etzioni
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作者：
Michele Banko;Oren Etzioni
传统信息提取 (IE) 采用关系名称和该关系的手动标记示例作为输入。 Open IE 是一种独立于关系的提取范例，专为 Web 等海量异构语料库量身定制。开放 IE 系统从文本中提取一组不同的关系元组，无需任何特定于关系的输入。打开 IE 是如何实现的？我们分析了英语句子的样本，以证明许多关系是使用一组紧凑的独立于关系的词汇句法模式来表达的，这些模式可以通过开放 IE 系统来学习。 Open IE 和传统 IE 之间的权衡是什么？我们在两个任务的背景下考虑这个问题。首先，当关系的数量很大，并且关系本身没有预先指定时，我们认为开放IE是必要的。然后，我们提出了一种名为 O-CRF 的 Open IE 新模型，并表明与之前最先进的 Open IE 系统 TEXTRUNNER 使用的模型相比，它实现了更高的精度和几乎两倍的召回率。其次，当目标关系的数量很少并且它们的名称预先已知时，我们表明 O-CRF 能够与传统提取系统相匹配的精度，尽管召回率要低得多。最后，我们展示了如何将两种类型的系统组合成一个混合系统，以实现比传统提取器更高的精度，并具有相当的召回率。
Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. Open IE is a relationindependent extraction paradigm that is tailored to massive and heterogeneous corpora such as the Web. An Open IE system extracts a diverse set of relational tuples from text without any relation-specific input. How is Open IE possible? We analyze a sample of English sentences to demonstrate that numerous relationships are expressed using a compact set of relation-independent lexico-syntactic patterns, which can be learned by an Open IE system. What are the tradeoffs between Open IE and traditional IE? We consider this question in the context of two tasks. First, when the number of relations is massive, and the relations themselves are not pre-specified, we argue that Open IE is necessary. We then present a new model for Open IE called O-CRF and show that it achieves increased precision and nearly double the recall than the model employed by TEXTRUNNER, the previous stateof-the-art Open IE system. Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a traditional extraction system, though at substantially lower recall. Finally, we show how to combine the two types of systems into a hybrid that achieves higher precision than a traditional extractor, with comparable recall.