  In this paper, we propose a novel representation for text documents based on
aggregating word embedding vectors into document embeddings. Our approach is
inspired by the Vector of Locally-Aggregated Descriptors used for image
representation, and it works as follows. First, the word embeddings gathered
from a collection of documents are clustered by k-means in order to learn a
codebook of semnatically-related word embeddings. Each word embedding is then
associated to its nearest cluster centroid (codeword). The Vector of
Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then
computed by accumulating the differences between each codeword vector and each
word vector (from the document) associated to the respective codeword. We plug
the VLAWE representation, which is learned in an unsupervised manner, into a
classifier and show that it is useful for a diverse set of text classification
tasks. We compare our approach with a broad range of recent state-of-the-art
methods, demonstrating the effectiveness of our approach. Furthermore, we
obtain a considerable improvement on the Movie Review data set, reporting an
accuracy of 93.3%, which represents an absolute gain of 10% over the
state-of-the-art approach. Our code is available at
https://github.com/raduionescu/vlawe-boswe/.
