Andrew Young

The company I work for makes heavy use of their IBM Power i midrange servers (previously known as AS/400 or iSeries servers). A lot of their software is written in the RPG programming language, which IBM originally developed back in the 1960s. The language was originally written to generate reports and lacked many “modern” programming features, such as IF statements and subroutines, which were added in RPG III.

Since starting at my current company, I’ve been trying to learn the current version of RPG, which is RPG IV (aka RPGLE or ILE/RPG). Most of the running code that I see in RPG is actually written using RPG III syntax despite the fact that RPG IV has been out since 1994. This is mostly due to the fact that much of it was either generated programmatically or was written before 1994. My goal in learning RPG isn’t to become proficient enough to program RPG for a living, but instead to become proficient enough to help our organization transition their existing systems to more modern technologies as needed. However, my “outsider” view of RPG (coming from a Java/Perl/Ruby/etc background) has helped me do some things with it that long time RPG programmers might not think of trying to do. This is an example of that.
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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:

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.)

This might be proof that I’m crazy:

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