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Large Scale Multi-label Text Classification with Semantic Word Vectors

Multi-label text classification has been applied to a multitude of tasks, including document indexing, tag suggestion, and sentiment classification. However, many of these methods disregard word order, opting to use bag-of-words models or TFIDF weighting to create document vectors. With the advent of powerful semantic embeddings, such as word2vec and GloVe, we explore how word embeddings and word order can be used to improve multi-label learning. Specifically, we explore how both a convolutional neural network (CNN) and a recurrent network with a gated recurrent unit (GRU) can independently be used with pre-trained word2vec embeddings to solve a large scale multi-label text classification problem. On a data set of over two million documents and 1000 potential labels, we demonstrate that both a CNN and a GRU provide substantial improvement over a Binary Relevance model with a bag-of-words representation.