@inproceedings{L16-1191,
 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.
},
 address = {Portorož, Slovenia},
 author = {Maximilian Köper and Melanie Zaiß and Qi Han and Steffen Koch and Sabine Schulte im Walde},
 booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
 month = {May},
 pages = {1202--1206},
 publisher = {European Language Resources Association (ELRA)},
 title = {Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification},
 url = {https://www.aclweb.org/anthology/L16-1191},
 year = {2016}
}