TY - GEN
T1 - Single-output recurrent neural networks for sentence binary classification
AU - Wicaksono, Alfan Farizki
AU - Adriani, Mirna
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/6
Y1 - 2017/3/6
N2 - We report several experiments on using Recurrent Neural Networks (RNNs) for sentence binary classification task. In terms of sentence classification, RNNs have an important advantage compared to well-known traditional machine learning models (e.g. SVM and Maximum Entropy), in which it can naturally take into account neighboring information between contiguous words. In addition, to perform binary classification task, we employed Single-Output RNNs (SORNNs) which only consists of a single output layer located in the last time step. The output layer itself is a vector consisting of two units (since we perform binary classification), in which each unit corresponds to a single label. Our results showed that SORNN achieved better performance than other traditional machine learning models, such as SVM, Maximum Entropy, and Naive Bayes, which have been widely used for sentence classification.
AB - We report several experiments on using Recurrent Neural Networks (RNNs) for sentence binary classification task. In terms of sentence classification, RNNs have an important advantage compared to well-known traditional machine learning models (e.g. SVM and Maximum Entropy), in which it can naturally take into account neighboring information between contiguous words. In addition, to perform binary classification task, we employed Single-Output RNNs (SORNNs) which only consists of a single output layer located in the last time step. The output layer itself is a vector consisting of two units (since we perform binary classification), in which each unit corresponds to a single label. Our results showed that SORNN achieved better performance than other traditional machine learning models, such as SVM, Maximum Entropy, and Naive Bayes, which have been widely used for sentence classification.
UR - http://www.scopus.com/inward/record.url?scp=85016944533&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2016.7872723
DO - 10.1109/ICACSIS.2016.7872723
M3 - Conference contribution
AN - SCOPUS:85016944533
T3 - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
SP - 293
EP - 296
BT - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Y2 - 15 October 2016 through 16 October 2016
ER -