Implementation of Eric Brill's "transformation-based learning" algorithm
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Implementation of Eric Brill's "transformation-based learning" algorithm with improvements

Nuts 'n' bolts

This C++ code is intended to be used as a static library. The demo program is largely my own hacking to test out what I've built.

The library code can be found in the /jcTBL subdirectory in /include and /source. You'll want to include the libjcTBL.a static library and include /jcTBL/include/jctbl.hpp, which brings in all the other include files.

The jctbl.hpp include file's header is loaded with background and advice on how to create and tune your own application.

I built this using Xcode 7.3.1 on a late 2013 iMac running OS X El Capitain. I'm using the GNU++11 [-std=gnu++11] language dialect and the libc++ (LLVM C++ standard library with C++11 support) library. This don't have reliances on Boost or any other libraries outside the STL. I haven't tried compiling this on Windows, Linux, or any other systems, but I see no reason why it wouldn't, given an appropriate makefile.

To run the demo program on a similar OS X system to mine, open jcTBL.xcodeproj in Xcode. From the Product menu, expand Scheme and choose "jcTBL_Demo". Edit Demo/main.cpp, find the main() routine, and change the data_path variable's value to point to the Data folder under the root of your project folder. Click the Run button on the toolbar.

About this project

I'm resuming my AI / NLP research. In studying different kinds of classifier systems I came across Eric Brill's concept of "transformation-based learning" (TBL). There are various TBL implementations out there, including fnTBL. Sadly, I was unable to compile their code and struggled to understand its structure. Moreover, I learn better by doing, so I decided to build my own from scratch using various documents describing the algorithm and enhancements by other researchers along the way.

After creating a first prototype, I decided to start from scratch with a cleaner implementation and an eye toward applying this to a variety of practical tasks, including tokenization, part of speech (PoS) tagging, chunking, named-entity recognition, and more. As of my first commit date (9/9/2016), I have only applied this to a first task of PoS tagging using training and test sets drawn from the Penn Treebank.

At heart, this is a direct implementation of Brill's original algorithm as he described it. As he and others have noted, the training process can be quite slow. I took inspiration from work by Mark Hepple (Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers) and Radu Florian and Grace Ngai (Transformation-Based Learning in the Fast Lane) in devising some performance enhancements, though. One big enhancement is built in support for multithreaded training. If you have 4 processor cores, you can set cls.training_threads to 4 and when you call cls.train(), it will keep all of them busy and run about 4 times faster. Set cls.use_best_rules to 10 and training will run about 10 times faster with potentially minimal loss of accuracy (or increase in number) of training rules devised. I also added in many low-level features like early bailing, hashing, and modest caching to optimize speed and memory usage.

One interesting innovation of mine is rule merging. The training algorithm, while studying the proposed rules that meet a minimum score threshold, may find two that can be combined into one rule. In one scenario, rule A is a superset of rule B, meaning what B changes, A already changes, so B gets deleted so as not to create a wasteful duplicate. This really only helps when cls.use_best_rules > 1. The other scenario is when two rules differ only by the value of exactly one "atom" of their predicate (input filters). In that case, one new rule is formed with that atom having the combined values from both rules. Since merging keeps repeating until there's nothing left to merge, these combined rules can acquire very long lists of values. The result, I've found in some PoS training tests, can be a reduction in the number of rules by a third to a half. While merging does increase the time it takes to train, having fewer rules to process can speed up classification at runtime, later. The accuracy of the rules seems about the same, though I found it may be marginally higher or lower than without merging. It seems to ever so slightly improve accuracy when cls.use_best_rules = 1. Here's an example of a merged rule:

PoS-1:MD & PoS+0:[JJ, NN, NNP, VBN, VBP] => VB 

One significant benefit of rule merging is that the resulting rules are easier to comprehend. If your goal is to use the rules generated to discover some otherwise invisible pattern in sequential data, Having ten rules merged into one makes that easier.

I purposely decided not to include support for reading and writing files because I didn't want to prejudice you, the developer, as to how and where you should store your own data.

Future goals

I've endeavored to make my code solid and especially free of memory leaks, but please do let me know if you find any bugs.

In the spirit of fnTBL, I might create a command line wrapper program at some point, but I'll admit I have little desire to right now. That seems like it would take a lot of time to do justice to and might mislead developers about how best to integrate this library into their own projects.

I wrote this to be completely cross-platform, though I built it specifically on OS X. I'd welcome having others build this on Windows, Linux or other systems and submitting their work so this can be distributed as a stand-alone library for all systems.

License and credit

This code was written entirely by Jim Carnicelli. I've given this project an MIT license, meaning you are free to use it pretty much however you want. That said, I would really appreciate it if you'd give credit to my contribution in your derived works. I'd also welcome you to drop me a line about how you are using it and if you've found interesting ways to improve upon it.