This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.
arXiv comments
This technical report is superseded by a paper entitled "Character-level Convolutional Networks for Text Classification", arXiv:1509.01626. It has considerably more experimental results and a rewritten introduction
Linked by 6 identifiable commenters in 6 comments across 6 threads, from Mar 2015 to Apr 2018. Found in 4 HN submissions from Feb 2015 to Feb 2017. Open submission discussions.
"Search is all well and good when we are counting words, which is what data analytics and machine learning are really all about." There are machine learning models which go far beyond counting words, for example see https://arxiv.org/abs/1502.01710
One example is this recent paper on learning (somewhat) high-level attributes of text from character streams alone (i.e., without telling the convolutional networks that things like words and punctuation exist). https://arxiv.org/abs/1502.01710
Is this the paper they said they based this on: "Text Understanding from Scratch": http://arxiv.org/abs/1502.01710 Here is example code for text classification from character-level using convolutional networks in Torch 7 https://github.com/zhangxiangxiao/Crepe
Word2vec is usually the standard neural word embeddings implementation. There are other algorithms as well such as glove[1], document embeddings[2] and backpropagation based methods[3]. Facebook just came out with a paper recently that beat word2vec as well[4]. Neural word…
"Search is all well and good when we are counting words, which is what data analytics and machine learning are really all about." There are machine learning models which go far beyond counting words, for example see https://arxiv.org/abs/1502.01710
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