ssubu

6 ms·

1.
Residential Segregation – A Simulation(ecnmst.gitlab.io)
12 points by ssubu 6y ago|1 comments
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by ssubu 7y ago
We speak about this is much more detail in this post ( https://0x65.dev/blog/2019-12-05/a-new-search-engine.html ), but in short, we prototyped our search initially with data we purchased from data-brokers. Once the
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by ssubu 7y ago
[Disclaimer: work at Cliqz] We do not crawl the web in the traditional sense, our search was bootstrapped on query logs. It is the very reason we could succeed in building a search engine with minimal resources, in comparison to our competi
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by ssubu 7y ago
Hi! Yes, we have played around with character and trigram level neural network language models. Also, we experimented with training a supervised neural network based on a misspellings dataset for the corrector. Unfortunately, we had trouble
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by ssubu 7y ago
Sure. cmputer gets corrected, not omputer. Just to provide some additional information, our library Keyvi( https://github.com/KeyviDev/Keyvi ) has a very fast implementation of an FST based spell correct.
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by ssubu 7y ago
[Disclaimer: work at Cliqz] Both techniques have their share of upsides and downsides, infact we also use an FST based model to perform splits i.e donaldtrump ---> donald trump. The problem with the FST approach is when the prefix has a
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by ssubu 7y ago
[Disclaimer: I work at Cliqz] We will have a blog post tomorrow on this very topic, but in short, we use a combination of Keyvi, Granne (both in-house) along with Cassandra and RocksDB. Though our approach mentioned in this blogpost signifi