• Authors: Martin Andrews

• Categories: cs.CL cs.LG

• Comments: 10 pages, 0 figures, submitted to ICONIP-2016.
• Previous experimental results were submitted to ICLR-2016, but the paper has been significantly updated, since a new experimental set-up worked much better

#### Abstract

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic. However, these vector space representations (created through large-scale text analysis) are typically stored verbatim, since their internal structure is opaque. Using word-analogy tests to monitor the level of detail stored in compressed re-representations of the same vector space, the trade-offs between the reduction in memory usage and expressiveness are investigated. A simple scheme is outlined that can reduce the memory footprint of a state-of-the-art embedding by a factor of 10, with only minimal impact on performance. Then, using the same ‘bit budget’, a binary (approximate) factorisation of the same space is also explored, with the aim of creating an equivalent representation with better interpretability.

### Accepted to ICONIP 2016!

I’ll do a separate posting when the presentation is done, the code is up, and I’m allowed to host the typo-fixed version of the arXiv paper.

Also, I’ll post the paper’s BiBTeX entry as soon as I get it.