Language as a matrix product state

This work addresses the problem of developing a novel statistical framework for natural language processing, potentially offering a new theoretical approach, but it appears incremental as it builds on existing concepts like matrix product states without clear application or validation.

The authors tackled the problem of modeling natural language statistically by proposing a model that treats language as a monoid and represents it in complex matrices with a translation-invariant probability measure, interpreting this measure as arising from a translation-invariant matrix product state via the Born rule.