Single-output recurrent neural networks for sentence binary classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages293-296
Number of pages4
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Country/TerritoryIndonesia
CityMalang
Period15/10/1616/10/16

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