TA: Gideon S. Mann
In the following, "the data" refers to both English and Czech, as usual.
Split the data in the following way: use last 40,000 words for testing (data S), and from the remaining data, use the last 20,000 for smoothing (data H, if any). Call the rest "data T" (training).
Download Eric Brill's supervised tagger,
either from his home page still at www.cs.jhu.edu/~brill, or directly
Install it (i.e., uncompress, untar, and make). If
you work on hops, you will need to do the following changes
in his package's Makefile before running make:
1. Add the following line:
CC = gcc
right after the first comment line in it, i.e. after the line beginning
# Makefile for Transformation...
2. Change all references to the cc compiler to $(CC) (there are 7 such references).
This change will enable the GNU C-compiler which is properly installed on hops, as opposed to the standard Sun's cc compiler, which will give you messages such as /usr/ucb/cc: language optional software package not installed.
After installation, get the data, train it on as much data from T as time allows (in the package, there is an extensive documentation on how to train it on new data), and evaluate on data S. Tabulate the results.
Do cross-validation of the results: split the data into S', [H',] T' such that S' is the first 40,000 words, and T' is the last but the first 20,000 words from the rest. Train Eric Brill's tagger on T' (again, use as much data as time allows) and evaluate on S'. Again, tabulate the results.
Do three more splits of your data (using the same formula: 40k/20k/the rest) in some way or another (as different as possible), and get another three sets of results. Compute the mean (average) accuracy and the standard deviation of the accuracy. Tabulate all results.
Now use only the first 10,000 words of T to estimate the initial (raw) parameters of the HMM tagging model. Strip off the tags from the remaining data T. Use the Baum-Welch algorithm to improve on the initial parameters. Smooth as usual. Evaluate your unsupervised HMM tagger and compare the results to the supervised HMM tagger.
Tabulate and compare the results of the HMM tagger vs. the Brill's tagger.