Relational Inference for Wikification
Relational Inference for Wikification
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DOI：
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发表时间：
2013
影响因子：
9.1
通讯作者：
Xiao Cheng;D. Roth
中科院分区：
文献类型：
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作者：
Xiao Cheng;D. Roth
维基化，通常称为维基百科消歧 (D2W)，是识别文本中的概念和实体并将其消歧为最具体的相应维基百科页面的任务。以前的 D2W 方法侧重于使用给定文本、维基百科文章及其链接结构的本地和全局统计数据，以评估可能候选列表之间的上下文兼容性。然而，当需要一定程度的文本理解来支持维基化时，这些方法就会失败（通常令人尴尬）。在本文中，我们介绍了一种新颖的维基化方法，结合统计方法和更丰富的文本关系分析。我们提供了一种可扩展、高效和模块化的维基化整数线性规划（ILP）公式，其中包含实体关系推理问题，并表明识别文本中关系的能力有助于候选生成和对维基百科标题进行排名。我们的结果显示维基化和 TAC 实体链接任务都有显着改进。
Wikification, commonly referred to as Disambiguation to Wikipedia (D2W), is the task of identifying concepts and entities in text and disambiguating them into the most specific corresponding Wikipedia pages. Previous approaches to D2W focused on the use of local and global statistics over the given text, Wikipedia articles and its link structures, to evaluate context compatibility among a list of probable candidates. However, these methods fail (often, embarrassingly), when some level of text understanding is needed to support Wikification. In this paper we introduce a novel approach to Wikification by incorporating, along with statistical methods, richer relational analysis of the text. We provide an extensible, efficient and modular Integer Linear Programming (ILP) formulation of Wikification that incorporates the entity-relation inference problem, and show that the ability to identify relations in text helps both candidate generation and ranking Wikipedia titles considerably. Our results show significant improvements in both Wikification and the TAC Entity Linking task.