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Title: Unsupervised measure of word similarity : how to outperform co-occurrence and vector cosine in VSMs
Authors: Santus, E
Chiu, TS
Lu, Q
Lenci, A
Huang, CR
Issue Date: 2016
Publisher: AAAI press
Source: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, p. 4260-4261 How to cite?
Abstract: In this paper, we claim that vector cosine - which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models - can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words. To prove it, we describe and evaluate APSyn, a variant of the Average Precision that, without any optimization, outperforms the vector cosine and the co-occurrence on the standard ESL test set, with an improvement ranging between +9.00% and +17.98%, depending on the number of chosen top contexts.
Description: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, US, 12-17 February 2016
ISBN: 9781577357605
Appears in Collections:Conference Paper

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