Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks

Nils Witt; Christin Seifert

doi:10.1007/978-3-319-67008-9_16

Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks

Nils Witt, Christin Seifert

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

17 Downloads (Pure)

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
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	https://doi.org/10.1007/978-3-319-67008-9_16
Publication status	Published - 2017
Externally published	Yes
Event	21st International Conference on Theory and Practice of Digital Libraries 2017 - Grand Hotel Palace, Thessaloniki, Thessaloniki, Greece Duration: 18 Sept 2017 → 21 Sept 2017 Conference number: 21 http://www.tpdl.eu/tpdl2017/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10450
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Theory and Practice of Digital Libraries 2017
Abbreviated title	TPDL 2017
Country/Territory	Greece
City	Thessaloniki
Period	18/09/17 → 21/09/17
Internet address	http://www.tpdl.eu/tpdl2017/

Keywords

n/a OA procedure

Access to Document

10.1007/978-3-319-67008-9_16

Cite this

Witt, N., & Seifert, C. (2017). Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks. In J. Kamps, G. Tsakonas, Y. Manolopoulos, L. Iliadis, & I. Karydis (Eds.), Research and Advanced Technology for Digital Libraries: 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings (pp. 193-204). (Lecture Notes in Computer Science; Vol. 10450). Springer. https://doi.org/10.1007/978-3-319-67008-9_16

Witt, Nils ; Seifert, Christin. / Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks. Research and Advanced Technology for Digital Libraries: 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings. editor / Jaap Kamps ; Giannis Tsakonas ; Yannis Manolopoulos ; Lazaros Iliadis ; Ioannis Karydis. Springer, 2017. pp. 193-204 (Lecture Notes in Computer Science).

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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.",

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Witt, N & Seifert, C 2017, Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks. in J Kamps, G Tsakonas, Y Manolopoulos, L Iliadis & I Karydis (eds), Research and Advanced Technology for Digital Libraries: 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10450, Springer, pp. 193-204, 21st International Conference on Theory and Practice of Digital Libraries 2017, Thessaloniki, Greece, 18/09/17. https://doi.org/10.1007/978-3-319-67008-9_16

Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks. / Witt, Nils; Seifert, Christin.
Research and Advanced Technology for Digital Libraries: 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings. ed. / Jaap Kamps; Giannis Tsakonas; Yannis Manolopoulos; Lazaros Iliadis; Ioannis Karydis. Springer, 2017. p. 193-204 (Lecture Notes in Computer Science; Vol. 10450).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Witt N, Seifert C. Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks. In Kamps J, Tsakonas G, Manolopoulos Y, Iliadis L, Karydis I, editors, Research and Advanced Technology for Digital Libraries: 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings. Springer. 2017. p. 193-204. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-67008-9_16

Understanding the Influence of Hyperparameters on Text Embeddings for Text Classification Tasks

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