@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},
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
%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