Directional distributional similarity for lexical expansion
L. Kotlerman, I. Dagan, I. Szpektor, and M. Zhitomirsky-Geffet. ACL-IJCNLP '09: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, page 69--72. Morristown, NJ, USA, Association for Computational Linguistics, (2009)
Abstract
Distributional word similarity is most commonly perceived as a symmetric relation. Yet, one of its major applications is lexical expansion, which is generally asymmetric. This paper investigates the nature of directional (asymmetric) similarity measures, which aim to quantify distributional feature inclusion. We identify desired properties of such measures, specify a particular one based on averaged precision, and demonstrate the empirical benefit of directional measures for expansion.
Description
ACL: ACL-IJCNLP '09, Directional distributional similarity for ...
%0 Conference Paper
%1 Kotlerman09directionalSimilarity
%A Kotlerman, Lili
%A Dagan, Ido
%A Szpektor, Idan
%A Zhitomirsky-Geffet, Maayan
%B ACL-IJCNLP '09: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
%C Morristown, NJ, USA
%D 2009
%I Association for Computational Linguistics
%K 09 Kotlerman directional distributional semantics similarity text
%P 69--72
%T Directional distributional similarity for lexical expansion
%U http://portal.acm.org/citation.cfm?id=1667583.1667606&coll=&dl=GUIDE&type=series&idx=SERIES11180&part=series&WantType=Proceedings&title=ACL
%X Distributional word similarity is most commonly perceived as a symmetric relation. Yet, one of its major applications is lexical expansion, which is generally asymmetric. This paper investigates the nature of directional (asymmetric) similarity measures, which aim to quantify distributional feature inclusion. We identify desired properties of such measures, specify a particular one based on averaged precision, and demonstrate the empirical benefit of directional measures for expansion.
@inproceedings{Kotlerman09directionalSimilarity,
abstract = {Distributional word similarity is most commonly perceived as a symmetric relation. Yet, one of its major applications is lexical expansion, which is generally asymmetric. This paper investigates the nature of directional (asymmetric) similarity measures, which aim to quantify distributional feature inclusion. We identify desired properties of such measures, specify a particular one based on averaged precision, and demonstrate the empirical benefit of directional measures for expansion.},
added-at = {2010-03-05T05:15:56.000+0100},
address = {Morristown, NJ, USA},
author = {Kotlerman, Lili and Dagan, Ido and Szpektor, Idan and Zhitomirsky-Geffet, Maayan},
biburl = {https://www.bibsonomy.org/bibtex/2e07f9454ffd90b30b4ea6282cbe5ee76/lee_peck},
booktitle = {ACL-IJCNLP '09: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers},
description = {ACL: ACL-IJCNLP '09, Directional distributional similarity for ...},
interhash = {4299d7aaf20f175f2792fe6280b69a9a},
intrahash = {e07f9454ffd90b30b4ea6282cbe5ee76},
keywords = {09 Kotlerman directional distributional semantics similarity text},
location = {Suntec, Singapore},
pages = {69--72},
publisher = {Association for Computational Linguistics},
timestamp = {2010-03-05T05:15:56.000+0100},
title = {Directional distributional similarity for lexical expansion},
url = {http://portal.acm.org/citation.cfm?id=1667583.1667606&coll=&dl=GUIDE&type=series&idx=SERIES11180&part=series&WantType=Proceedings&title=ACL},
year = 2009
}