@inproceedings{romeo-etal-2014-choosing,
title = "Choosing which to use? A study of distributional models for nominal lexical semantic classification",
author = "Romeo, Lauren and
Lebani, Gianluca and
Bel, N{\'u}ria and
Lenci, Alessandro",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/583_Paper.pdf",
pages = "4366--4373",
abstract = "This paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task. We consider models that exploit various types of distributional features, which thereby provide different representations of nominal behavior in context. The experiments presented in this work demonstrate the advantages and disadvantages of each model considered. This analysis also considers a combined strategy that we found to be capable of leveraging the bottlenecks of each model, especially when large robust data is not available.",
}
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%0 Conference Proceedings
%T Choosing which to use? A study of distributional models for nominal lexical semantic classification
%A Romeo, Lauren
%A Lebani, Gianluca
%A Bel, Núria
%A Lenci, Alessandro
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F romeo-etal-2014-choosing
%X This paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task. We consider models that exploit various types of distributional features, which thereby provide different representations of nominal behavior in context. The experiments presented in this work demonstrate the advantages and disadvantages of each model considered. This analysis also considers a combined strategy that we found to be capable of leveraging the bottlenecks of each model, especially when large robust data is not available.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/583_Paper.pdf
%P 4366-4373
Markdown (Informal)
[Choosing which to use? A study of distributional models for nominal lexical semantic classification](http://www.lrec-conf.org/proceedings/lrec2014/pdf/583_Paper.pdf) (Romeo et al., LREC 2014)
ACL