Merge and refactor changes made by Filipe regarding stopwords processing.
Instead of using a hardcoded list of stopwords in the Java class, we read
them all from files. That will make the update process easier (and also
free us from Java's 64k limit for compiled class size).

Several changes were also made regarding parsing and writing files.
There are two methods to that: one to read data and other to write
date. The major change, actually, is regarding the correctness of the
implementation: we are now fully compatible with DTM format. The old
approach was broken: documents were not ordered by date and terms for
each document were being incorrectly accounted. Terms are now also
identified by numbers from 1 to n (thus compatible with the assumption
that the line number is the term identifier).

Furthermore, the program now generates two extra files: one with terms
and another with documents. Hopefully, we will be able to use them
to fully understand the data DTM provides us.
Stemmize stopwords before removing stopwords.
Replace Lucene by lode-miner. Now we have finer control of how to preprocess
the text extracted from BibTeX.

Remove dependency to Apache Commons-Collection (we use plain Java classes for
that now).

Correctly generated data as required by DTM (Dynamic Topic Modeling).

Add a new preprocessor based on the pipeline architecture provided by Lode-Miner.
Fix generation of DTM output. Actually, it was a bug into Lode-Miner.
Although a simple error, it dramatically change the final result.

Make most of the pipeline 'word stripping' components as conditionals
(using ConditionalDups).
Create test case for BibTeX2DTM. This test case is handy to debug
parsing and output.
Adding processor of DTM outputs
Properly integrate analysis of topics evolution (Util.java). We
have now a ScienceEvolution class that prepares the dataset,
run DTM (cross-platform(ily)) and outputs the detected topics.

Remove dependency upon Lucene (we use our own preprocessor since
last week).
Improve stopwords list and remove words using NLP.
Implement a new preprocessor that allows just words similar to those
at CSCW-2.txt. For each word within that file, it add synonyms derived
from WordNet. The name of this preprocessor ir AlternativeTextPipelinePreprocessor.

The previous preprocessor is available as DefaultTextPipelinePreprocessor. You
may use it instead (you just have to instance it instead of the alternative one).
The default preprocessor also employs a NLP strategy to remove even more words.

As of DTM, we can define more parameters now: minIterations, maxIterations, and
alpha.
Chage in Util.java in order to adjust the ordering of probabilities of terms in topics.