@inproceedings{koper-etal-2016-visualisation,
title = "Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification",
author = {K{\"o}per, Maximilian and
Zai{\ss}, Melanie and
Han, Qi and
Koch, Steffen and
Schulte im Walde, Sabine},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1191",
pages = "1202--1206",
abstract = "Vector space models and distributional information are widely used in NLP. The models typically rely on complex, high-dimensional objects. We present an interactive visualisation tool to explore salient lexical-semantic features of high-dimensional word objects and word similarities. Most visualisation tools provide only one low-dimensional map of the underlying data, so they are not capable of retaining the local and the global structure. We overcome this limitation by providing an additional trust-view to obtain a more realistic picture of the actual object distances. Additional tool options include the reference to a gold standard classification, the reference to a cluster analysis as well as listing the most salient (common) features for a selected subset of the words.",
}
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%0 Conference Proceedings
%T Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification
%A Köper, Maximilian
%A Zaiß, Melanie
%A Han, Qi
%A Koch, Steffen
%A Schulte im Walde, Sabine
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F koper-etal-2016-visualisation
%X Vector space models and distributional information are widely used in NLP. The models typically rely on complex, high-dimensional objects. We present an interactive visualisation tool to explore salient lexical-semantic features of high-dimensional word objects and word similarities. Most visualisation tools provide only one low-dimensional map of the underlying data, so they are not capable of retaining the local and the global structure. We overcome this limitation by providing an additional trust-view to obtain a more realistic picture of the actual object distances. Additional tool options include the reference to a gold standard classification, the reference to a cluster analysis as well as listing the most salient (common) features for a selected subset of the words.
%U https://aclanthology.org/L16-1191
%P 1202-1206
Markdown (Informal)
[Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification](https://aclanthology.org/L16-1191) (Köper et al., LREC 2016)
ACL