# Learning Language from a Large (Unannotated) Corpus

arXiv: https://arxiv.org/abs/1401.3372 · HTML: https://mlsanity.com/paper/1401.3372/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2014.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Tokenization Learning](https://mlsanity.com/paper/2205.11443/) (2022)
- [Self-tuning hyper-parameters for unsupervised cross-lingual tokenization](https://mlsanity.com/paper/2303.02427/) (2023)
- [Evolution of Efficient Symbolic Communication Codes](https://mlsanity.com/paper/2306.02383/) (2023)
- [On Unsupervised Training of Link Grammar Based Language Models](https://mlsanity.com/paper/2208.13021/) (2022)

## Abstract (arXiv)

A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.