Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification

Maximilian Köper, Melanie Zaiß, Qi Han, Steffen Koch, Sabine Schulte im Walde

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.
Anthology ID:
L16-1191
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1202–1206
Language:
URL:
https://aclanthology.org/L16-1191
DOI:
Bibkey:
Cite (ACL):
Maximilian Köper, Melanie Zaiß, Qi Han, Steffen Koch, and Sabine Schulte im Walde. 2016. Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1202–1206, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification (Köper et al., LREC 2016)
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