Abstract
Many applications in the natural language processing domain require the tuning of machine learning algorithms, which involves adaptation of hyperparameters. We perform experiments by systematically varying hyperparameter settings of text embedding algorithms to obtain insights about the influence and interrelation of hyperparameters on the model performance on a text classification task using text embedding features. For some parameters (e.g., size of the context window) we could not find an influence on the accuracy while others (e.g., dimensionality of the embeddings) strongly influence the results, but have a range where the results are nearly optimal. These insights are beneficial to researchers and practitioners in order to find sensible hyperparameter configurations for research projects based on text embeddings. This reduces the parameter search space and the amount of (manual and automatic) optimization time.
Original language | English |
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Title of host publication | Research and Advanced Technology for Digital Libraries |
Subtitle of host publication | 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings |
Editors | Jaap Kamps, Giannis Tsakonas, Yannis Manolopoulos, Lazaros Iliadis, Ioannis Karydis |
Publisher | Springer |
Pages | 193-204 |
ISBN (Electronic) | 978-3-319-67008-9 |
ISBN (Print) | 978-3-319-67007-2 |
DOIs | |
Publication status | Published - 2017 |
Event | 21st International Conference on Theory and Practice of Digital Libraries 2017 - Grand Hotel Palace, Thessaloniki, Thessaloniki, Greece Duration: 18 Sep 2017 → 21 Sep 2017 Conference number: 21 http://www.tpdl.eu/tpdl2017/ |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 10450 |
Conference
Conference | 21st International Conference on Theory and Practice of Digital Libraries 2017 |
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Abbreviated title | TPDL 2017 |
Country/Territory | Greece |
City | Thessaloniki |
Period | 18/09/17 → 21/09/17 |
Internet address |