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We have been working on improving our Non-Metric Space Library (NMSLIB), a toolkit for searching in generic spaces. First we carried out a new evaluation for a reasonably diverse data set. The results recently appeared in PVLDB 15.

Second, we participated in a public evaluation. The results confirmed that our implementations are quite competitive. More specifically, the small-world graph approach proposed by Malkov et al. fared very well against FLANN and kgraph.

However, our work is far from being finished. We may now attempt to apply our toolkit to NLP problems. I summarized thoughts on this topic in a talk at ML lunch.

Equally important we try to make our toolkit easier to use. Because we originally cared mostly about efficiency of experimentation and publishing, a few important features are missing. We are now trying to fill the gap.

In addition, we plan to carry out more comprehensive evaluations that would allow us to better understand the problem at hand as well as to devise methods that work well for a broader class of non-metric spaces.