Sometimes we want to modify a dataset, but doing so requires knowing global statistics for the dataset itself. In this case we need to import a data set and then transform it to create a new file.
To remove low frequency words, we can specify a minimum number of occurrences.
bin/mallet prune --input input.seq --output pruned.seq --prune-count 5
In most natural language collections removing very infrequent words can have a large effect on the vocabulary size, but relatively little effect on the number of tokens.
You can also prune based on document frequency with
which may be more robust in cases where one document has a large number of instances of a word that otherwise never occurs.
To remove high frequency words, you can use inverse document frequency (IDF).
A simple way to think of IDF is that if a word occurs in 1 of every 10 documents, its IDF will be
log(10) = 2.3.
The IDF of a word that occurs in 1 of every 100 = 102 documents will have IDF twice that, 4.6, and 1 in 1000 will be 6.9.
bin/mallet prune --input input.seq --output pruned.seq --min-idf 4.6
Remember that IDF is inverse, so to remove frequent words set the min IDF, and to remove rare words set the max IDF with
Another way to remove words is to create a custom stoplist. This command will print the token and document counts in tab delimited format for an instance list:
bin/mallet info --input input.seq --print-feature-counts
If you have access to bash-style commands, one thing I do all the time is:
bin/mallet info --input sagas_stopped.seq --print-feature-counts | sort -nr -k 3 | head -100 | cut -f 1 > stoplist.txt
This sorts the words in descending order by document frequency, takes the first 100, grabs just the first column (the word itself) and saves it as
I then edit that file manually to make judgements about what I want to keep or not, and to see if there are errors or unexpected words that I might want to know about.
Another common task is to split an instance list into multiple sets, for example to train a model and then test its generalization performance.
bin/mallet split --input input.seq --training-file train.seq --testing-file test.seq --training-portion 0.7
This will copy a random 70% of the instances from the
input.seq file into
train.seq and the rest into
Remember that you are responsible for ensuring that there are not duplicate instances that are in both sets!
prune are actually the same class, so you can do both operations together.
This class implements the method from “Authorless Topic Models” by Thompson and Mimno, COLING 2018.
The goal is to reduce the frequency of words that are unusually associated with a particular label. This is useful as a pre-processing step for topic modeling becuase it reduces the correlation of topics to known class labels. The problem comes up most often in fiction, where topics tend to simply reproduce lists of characters.
The input is a labeled feature sequence, of the sort used for topic modeling. Unlike the regular topic modeling system, labels are required, since we need something to correlate.
The output is another feature sequence with word tokens removed. Note that some words may disappear from the corpus, but they will still be present in the alphabet.
The code takes one parameter, equivalent to a p-value where the null hypothesis is that a word occurs no more frequently in one category than in the collection as a whole.
bin/mallet run cc.mallet.transform.DownsampleLabelWords --input input.seq --output downsampled.seq
This process will by default write a file documenting the number of times each word was removed from each label. Multiple and overlapping labels are currently not supported.