In this post I show you a few (sort of) neighbours of « yellow ». I compare the neighbourhood of the word vectors obtained from LSI (or more precisely my fast variant of LSI) and using my method « NC-ISC ». The original cooccurrence matrix has been obtained from a merge of Europarl corpus and the BNC. No preprocessing have been applied to the corpus, not even lowercasing.

The vocabulary size is about 520000, the matrix has been transformed with positive pointwise mutual information.

The cooccurrence relation is simply « the next word ». Other relations could be used (including classical word windows), but I have chosen to show you this relation because it seems to be the most basic possible.

To sum up, the 520000*520000 cooccurrence matrix containing about 25M entries has been transformed into a 192 features-words dictionary.

Here are the (long, sorry) lists obtained

• on the left, using my method (NC-ISC)
• on the right, using the equivalent of a simple LSI (i.e. a thin SVD)

Things are getting a bit weird with LSI from the 100th row, approximately. On the other hands, NC-ISC keeps listing colors, aspects (visual then physical), for quite a long time. I’m not a native English speaker, so if you want to list the « acceptable » neighbours from the bad ones, you’re welcome !