Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.