  Modern machine learning applications require huge artificial networks
demanding in computational power and memory. Light-based platforms promise
ultra-fast and energy-efficient hardware, which may help in realizing
next-generation data processing devices. However, current photonic networks are
limited by the number of input-output nodes that can be processed in a single
shot. This restricted network capacity prevents their application to relevant
large-scale problems such as natural language processing. Here, we realize a
photonic processor with a capacity exceeding $1.5 \times 10^{10}$ optical
nodes, more than one order of magnitude larger than any previous
implementation, which enables photonic large-scale text encoding and
classification. By exploiting the full three-dimensional structure of the
optical field propagating in free space, we overcome the interpolation
threshold and reach the over-parametrized region of machine learning, a
condition that allows high-performance natural language processing with a
minimal fraction of training points. Our results provide a novel solution to
scale-up light-driven computing and open the route to photonic language
processing.
