Gender Classification of Web Authors Using Feature Selection and Language Models

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Standard

Gender Classification of Web Authors Using Feature Selection and Language Models. / Aravantinou, Christina; Simaki, Vasiliki; Mporas, Iosif; Megalooikonomou, Vasileios.

Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings. red. / Andrey Ronzhin; Rodmonga Potapova; Nikos Fakotakis. Springer, 2015. s. 226-233 (Lecture Notes in Computer Science; Vol. 9319).

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Harvard

Aravantinou, C, Simaki, V, Mporas, I & Megalooikonomou, V 2015, Gender Classification of Web Authors Using Feature Selection and Language Models. i A Ronzhin, R Potapova & N Fakotakis (red), Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9319, Springer, s. 226-233. https://doi.org/10.1007/978-3-319-23132-7_28

APA

Aravantinou, C., Simaki, V., Mporas, I., & Megalooikonomou, V. (2015). Gender Classification of Web Authors Using Feature Selection and Language Models. I A. Ronzhin, R. Potapova, & N. Fakotakis (Red.), Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings (s. 226-233). (Lecture Notes in Computer Science; Vol. 9319). Springer. https://doi.org/10.1007/978-3-319-23132-7_28

CBE

Aravantinou C, Simaki V, Mporas I, Megalooikonomou V. 2015. Gender Classification of Web Authors Using Feature Selection and Language Models. Ronzhin A, Potapova R, Fakotakis N, redaktörer. I Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings. Springer. s. 226-233. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-23132-7_28

MLA

Aravantinou, Christina et al. "Gender Classification of Web Authors Using Feature Selection and Language Models"., Ronzhin, Andrey Potapova, Rodmonga Fakotakis, Nikos (redaktörer). Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings. Lecture Notes in Computer Science. Springer. 2015, 226-233. https://doi.org/10.1007/978-3-319-23132-7_28

Vancouver

Aravantinou C, Simaki V, Mporas I, Megalooikonomou V. Gender Classification of Web Authors Using Feature Selection and Language Models. I Ronzhin A, Potapova R, Fakotakis N, redaktörer, Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings. Springer. 2015. s. 226-233. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-23132-7_28

Author

Aravantinou, Christina ; Simaki, Vasiliki ; Mporas, Iosif ; Megalooikonomou, Vasileios. / Gender Classification of Web Authors Using Feature Selection and Language Models. Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings. redaktör / Andrey Ronzhin ; Rodmonga Potapova ; Nikos Fakotakis. Springer, 2015. s. 226-233 (Lecture Notes in Computer Science).

RIS

TY - GEN

T1 - Gender Classification of Web Authors Using Feature Selection and Language Models

AU - Aravantinou, Christina

AU - Simaki, Vasiliki

AU - Mporas, Iosif

AU - Megalooikonomou, Vasileios

PY - 2015

Y1 - 2015

N2 - In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of gender classification, regardless of the type of the machine learning algorithm used.

AB - In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of gender classification, regardless of the type of the machine learning algorithm used.

KW - Text classification

KW - Gender identification

KW - Feature selection

U2 - 10.1007/978-3-319-23132-7_28

DO - 10.1007/978-3-319-23132-7_28

M3 - Paper in conference proceeding

SN - 978-3-319-23131-0

T3 - Lecture Notes in Computer Science

SP - 226

EP - 233

BT - Speech and Computer

A2 - Ronzhin, Andrey

A2 - Potapova, Rodmonga

A2 - Fakotakis, Nikos

PB - Springer

ER -