Tag Archives: japanese

First Release of Japanese Dependency Vectors

At the end of last semester I finished the first version of Japanese Dependency Vectors (jpdv).  I had to give up on using Clojure at the last minute because it was taking me too long to make progress and I needed to have some sort of a working system to turn in for my NLP final project.

To accomplish this I rewrote jpdv in Java.  It took me about 18 hours of solid coding, minus time for food of course.

The software can now generate both context-based and dependency-based vector spaces for Japanese text that has been pre-parsed with CaboCha.  It can also generate a similarity matrix for a given vector space using the cosine similarity measurement.  I still need to add a path selection function to throw out paths that are too long and a basis element selection function that determines which N basis elements to keep out of all those discovered, but I will add those to the next release.  I’m thinking of writing the path selection and basis element selection functions as Groovy scripts so that they can be supplied at run time.  This would allow for better customization of the system at run time for a given task.

More information can be found here and on the GitHub page.

Here is an example similarity matrix generated by the current version of jpdv:

WORD コンピュータ 兄弟 赤い 電話 青い 黒い
コンピュータ 1.00000 0.06506 0.07563 0.00000 0.07760 0.00000 0.00000
兄弟 0.06506 1.00000 0.19929 0.00000 0.14947 0.00000 0.00000
0.07563 0.19929 0.99999 0.00000 0.19833 0.00000 0.00000
赤い 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.01352
電話 0.07760 0.14947 0.19833 0.00000 1.00000 0.00000 0.00000
青い 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000
黒い 0.00000 0.00000 0.00000 0.01352 0.00000 0.00000 1.00000

Japanese Dependency Vectors

I’ve been working on a new project I call “Japanese Dependency Vectors” or “jpdv” for short.  It’s a program that generates  dependency based semantic vector spaces for Japanese text.  (There’s already an excellent tool for doing this with English, which was written by Sebastian Pado.)

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.)

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