However, jpdv still has a way to go before it works as promised.  So far the tool can parse CaboCha formatted XML and produce both a word co-occurrence based vector space and a slightly modified XML representation that better demonstrates the dependency relationships of the words in the text.  The next step is to use the dependency information to produce the vector space that I need.  Unfortunately, I only have until the end of next week to finish it, because I’m working on this as the final project in my NLP class this semester.  I also plan to use the vector spaces created by the tool to do word sense disambiguation for the SEMEVAL-2 shared task on Japanese WSD.

(The image included here was generated by jpdv as a LaTeX file from one of the sentences I’m using for testing.)

I’m working on a project for my NLP class that involves generating a semantic vector space for Japanese text, and I decided that this might be a good time to learn one of the LISP dialects.  I’ve been looking at Clojure for a while now, but I hadn’t taken the time to learn it before.  I must say, I’m quite impressed so far.  The fact that reading a Japanese XML document into a data structure “just works” without any tweaking is pretty nice.  I’m still coming to grips with functional programming, but I’m liking it so far.  And the best thing as far as I’m concerned is that Clojure code can easily be made massively parallel thanks to all of its data types being immutable (ala Erlang).  That will help me out a lot when I need to run my code on multi-core / multi-processor machines (which I often have to do because of the sheer amount of data being crunched.)