2014 OpenQuestionAnsweringoverCurate

• (Fader et al., 2014) ⇒ Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. (2014). “Open Question Answering over Curated and Extracted Knowledge Bases.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623677

Subject Headings: Open Question Answering.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Open+Question+Answering+over+Curated+and+Extracted+Knowledge+Bases
• http://dl.acm.org/citation.cfm?id=2623330.2623677&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; experimentation; language parsing and understanding

Abstract

We consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this paper, we present OQA, the first approach to leverage both curated and extracted KBs.

A key technical challenge is designing systems that are robust to the high variability in both natural language questions and massive KBs. OQA achieves robustness by decomposing the full Open QA problem into smaller sub-problems including question paraphrasing and query reformulation. OQA solves these sub-problems by mining millions of rules from an unlabeled question corpus and across multiple KBs. OQA then learns to integrate these rules by performing discriminative training on question-answer pairs using a latent-variable structured perceptron algorithm. We evaluate OQA on three benchmark question sets and demonstrate that it achieves up to twice the precision and recall of a state-of-the-art Open QA system.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 OpenQuestionAnsweringoverCurate	Oren Etzioni Anthony Fader Luke Zettlemoyer			Open Question Answering over Curated and Extracted Knowledge Bases				10.1145/2623330.2623677		2014