# Language as a matrix product state

arXiv: https://arxiv.org/abs/1711.01416 · HTML: https://mlsanity.com/paper/1711.01416/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2017.

## Built on by (most cited first, 7 of 7)

- [Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework](https://mlsanity.com/paper/2106.15666/) (2021)
- [Language Modeling Using Tensor Trains](https://mlsanity.com/paper/2405.04590/) (2024)
- [Tensor network to learn the wavefunction of data](https://mlsanity.com/paper/2111.08014/) (2021)
- [Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics](https://mlsanity.com/paper/2404.11768/) (2024)
- [Infinitely Wide Tensor Networks as Gaussian Process](https://mlsanity.com/paper/2101.02333/) (2021)
- [Group-invariant tensor train networks for supervised learning](https://mlsanity.com/paper/2206.15051/) (2022)
- [Entangling Machine Learning with Quantum Tensor Networks](https://mlsanity.com/paper/2403.12969/) (2024)

## Abstract (arXiv)

We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure. We interpret the probability measure as arising via the Born rule from a translation invariant matrix product state.