Paper Info
Title
Multi-input CNN for Text Classification in Commercial Scenarios.
Abstract
In this work we describe a multi-input Convolutional Neural Network for text classification which allows for combining text preprocessed at word level, byte pair encoding level and character level. We conduct experiments on different datasets and we compare the results obtained with other classifiers. We apply the developed model to two different practical use cases: (1) classifying ingredients into their corresponding classes by means of a corpus provided by Northfork; and (2) classifying texts according to the English level of their corresponding writers by means of a corpus provided by ProvenWord. Additionally, we perform experiments on a standard classification task using Yahoo! Answers and GermEval2017 task A datasets. We show that the developed architecture obtains satisfactory results with these corpora, and we compare results obtained for each dataset with different state-of-the-art approaches, obtaining very promising results.
Year
DOI
Venue
2019
10.1007/978-3-030-20521-8_49
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT I
Keywords
Field
DocType
Text classification,Document classification,CNN,Multi-input network,Gastrofy,ProvenWord,Use case,Northfork,GermEval2017,Agglutinative language,Swedish,German
Document classification,Use case,Pattern recognition,Convolutional neural network,Computer science,Agglutinative language,Byte pair encoding,Artificial intelligence
Conference
Volume
ISSN
Citations 
11506
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zuzanna Parcheta112.09
Germán Sanchis-Trilles210116.95
francisco casacuberta31439161.33
Robin Redahl400.34