Paper Info
Title
Character-Based Text Classification using Top Down Semantic Model for Sentence Representation.
Abstract
Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other character-based and word-based convolutional neural network models by cite{zhang15} across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.
Year
Venue
Field
2017
arXiv: Computation and Language
Pattern recognition,Convolutional neural network,Linear model,Computer science,Recurrent neural network,Natural language processing,Artificial intelligence,Deep learning,Sentence,Semantics,Semantic role labeling,Semantic data model
DocType
Volume
Citations 
Journal
abs/1705.10586
1
PageRank 
References 
Authors
0.35
14
3
Name
Order
Citations
PageRank
Zhenzhou Wu151.41
Xin Zheng226418.79
Daniel Dahlmeier346029.67