HaotianMXu/Multimodal-CNNs
Implementation of the ACM-BCB 2016 paper Text Classification with Topic-based Word Embedding and Convolutional Neural Networks
This project helps researchers classify text documents by category. It takes raw text data and pre-trained word embeddings, then outputs classifications based on learned topics within the text. This is designed for academic researchers in natural language processing or bioinformatics who need to categorize textual information.
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Use this if you are a researcher studying novel text classification methods, particularly those involving topic modeling and convolutional neural networks.
Not ideal if you need a user-friendly, out-of-the-box solution for general text classification, or if you are not comfortable with Python 2.7 and specific deep learning frameworks.
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Python
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Last pushed
Oct 03, 2017
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