(Invited talk at Search Solutions 2015)
A lot of recent work in neural models and “Deep Learning” is focused on learning vector representations for text, image, speech, entities, and other nuggets of information. From word analogies to automatically generating human level descriptions of images, the use of text embeddings has become a key ingredient in many natural language processing (NLP) and information retrieval (IR) tasks.
In this talk, I will present some personal learnings from working on (neural and non-neural) text embeddings for IR, as well as highlight a few key recent insights from the broader academic community. I will talk about the affinity of certain embeddings for certain kinds of tasks, and how the notion of relatedness in an embedding space depends on how the vector representations are trained. The goal of this talk is to encourage everyone to start thinking about text embeddings beyond just as an output of a “black box” machine learning model, and to highlight that the relationships between different embedding spaces are about as interesting as the relationships between items within an embedding space.