2012 LargeScaleLearningofWordRelated

• (Halawi et al., 2012) ⇒ Guy Halawi, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. (2012). “Large-scale Learning of Word Relatedness with Constraints.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339751

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Large-scale+Learning+of+Word+Relatedness+with+Constraints
• http://dl.acm.org/citation.cfm?id=2339530.2339751&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; semantic similarity; word relatedness

Abstract

Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related [[words impose constraint]]s on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 LargeScaleLearningofWordRelated	Evgeniy Gabrilovich Yehuda Koren Gideon Dror Guy Halawi			Large-scale Learning of Word Relatedness with Constraints				10.1145/2339530.2339751		2012