You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Jan 2, 2019. It is now read-only.
The essence of the cortical API is just mapping words into fixed-length sparse bit vectors. You can get the same functionality with dense vectors. The most famous famous algorithms are for example implemented in Gensim which allows for subsequent:
clustering to discover common types of documents
(approximate) nearest neighbor search to form recommendations for similar tables
semantic search (e.g. "doctors +
The main "secret sauce" is to do efficient matrix decomposition on term-frequencies around the word in focus (original paper by Mikolov et. al 2013, good explanation on Quora).
Many pre-learnt word vectors on different corpora exist (Wikipedia, news articles, etc.). Thus, it's feasible to just load such a dictionary once and put them on a server and avoid dependency on Cortical. This includes basic operations such as averaging on bag-of-words.
A ready made server implementation is from 3Top: https://github.com/3Top/word2vec-api
If we need more sophisticated NLP with syntactic parsing, e.g. to allow disambiguation of words depending on their context, we will extend the API-fy with this library.
The text was updated successfully, but these errors were encountered:
The essence of the cortical API is just mapping words into fixed-length sparse bit vectors. You can get the same functionality with dense vectors. The most famous famous algorithms are for example implemented in Gensim which allows for subsequent:
The main "secret sauce" is to do efficient matrix decomposition on term-frequencies around the word in focus (original paper by Mikolov et. al 2013, good explanation on Quora).
Many pre-learnt word vectors on different corpora exist (Wikipedia, news articles, etc.). Thus, it's feasible to just load such a dictionary once and put them on a server and avoid dependency on Cortical. This includes basic operations such as averaging on bag-of-words.
A ready made server implementation is from 3Top: https://github.com/3Top/word2vec-api
If we need more sophisticated NLP with syntactic parsing, e.g. to allow disambiguation of words depending on their context, we will extend the API-fy with this library.
The text was updated successfully, but these errors were encountered: