* Neural Networks are used in unsupervised learning to learn better representations of the input data.
* Neural networks can learn a mapping from document to real-valued vector in such a way that resulting vectors are similar for documents with similar content. This can be achieved using autoencoders which is a model that is trained to reconstruct the original vector from a smaller representation with reconstruction error as a cost function.
* There are neural networks that are specifically designed for clustering as well. The most widely known is the self-organizing maps (SOM).
