Name
Title | ||
|---|---|---|
Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification. |
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. |
Year | Venue | Keywords |
|---|---|---|
2016 | LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | vectors space models,high-dimensional features,interactive visualisation |
Field | DocType | Citations |
Visualization,Computer science,Natural language processing,Artificial intelligence | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maximilian Köper | 1 | 27 | 6.18 |
| Melanie Zaiß | 2 | 0 | 0.34 |
| Qi Han | 3 | 11 | 4.90 |
| Steffen Koch | 4 | 341 | 26.58 |
| Sabine Schulte im Walde | 5 | 440 | 65.65 |