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Clustering of Verbal Fluency responses.

Project Description

This package is designed to generate clustering analyses for
transcriptions of semantic and phonemic verbal fluency test responses.
In a verbal fluency test, the subject is given a set amount of time
(usually 60 seconds) to name as many words as he or she can that
correspond to a given specification. For a phonemic test, subjects are
asked to name words that begin with a specific letter. For a semantic
fluency test, subjects are asked to provide words of a certain category,
e.g. animals. VFClust groups words in responses based on phonemic or
semantic similarity, as described below. It then calculates metrics
derived from the discovered groups and returns them as a CSV file or
Python dict object. For a detailed explanation of the reasoning underlying
the computation of these measures, please see:

Ryan et al., Computerized Analysis of a Verbal Fluency Test\_acl2013.pdf

Verbal fluency tests are often used in test batteries used to study
cognitive impairment arising from e.g. Alzheimer's disease, Parkinson's
disease, and certain medications. The following reference provides
an introduction to the use of clustering in cognitive evaluation.

Mayr, U. (2002). On the dissociation between clustering and switching in
verbal fluency: Comment on Troyer, Moscovitch, Winocur, Alexander and
Stuss. Neuropsychologia, 40(5), 562-566.

Clustering in VFClust

VFClust finds adjacent subsets of words of the following types:

- **clusters**: every entry in a cluster is sufficiently similar to every
other entry

- **chains**: every entry in a chain is sufficiently similar to adjacent

where "entry" corresponds to a word, compound word, or multiple adjacent
words with the same stem.

Similarity scores between words are thresholded and binarized using
empirically-derived thresholds. Overlap of clusters is allowed (a word
can be part of multiple clusters), but overlapping chains are not
possible, as any two adjacent words with a lower-than-threshold
similarity breaks the chain. Clusters subsumed by other clusters are not

The similarity measures used are the following:

- **PHONETIC/"phone"**: the phonetic similarity score (PSS) is calculated
between the phonetic representations of the input units. It is equal
to 1 minus the Levenshtein distance between two strings, normalized
to the length of the longer string. The strings should be compact
phonetic representations of the two words. (This method is a
modification of a Levenshtein distance function available at

- **PHONETIC/"biphone"**: the binary common-biphone score (CBS) depends on
whether two words share their initial and/or final biphone (i.e., set
of two phonemes). A score of 1 indicates that two words have the same
intial and/or final biphone; a score of 0 indicates that two words
have neither the same initial nor final biphone This is also
calculated using the phonetic representation of the two words.

- **SEMANTIC/"lsa"**: a semantic relatedness score (SRS) is calculated as
the COSINE of the respective term vectors for the first and second
word in an LSA space of the specified clustering\_parameter. Unlike
the PHONETIC methods, this method uses the .text property of the
input Unit objects.

- **SEMANTIC/"custom"**: the user can specify a custom file of word similarities,
in which each pair of words is given a custom similarity score.


After chains/clusters are discovered using the methods relevant for the
type of fluency test performed, metrics are derived from the clusters
and output to screen and a .csv file (if run as a script) or to a python
dict object (if run as a package). The following metrics are calculated:

Counts of different token types in the raw input. Each of these is
prefaced by ''COUNT\_'' in the output.

- **total\_words**: count of words (i.e. utterances with semantic content)
spoken by the subject. Filled pauses, silences, coughs, breaths,
words by the interviewer, etc. are all excluded from this count.

- **permissible\_words**: Number of words spoken by the subject that
qualify as a valid response according to the clustering criteria.
Compound words are counted as a single word in SEMANTIC clustering,
but as two words in PHONETIC clustering.

- **exact\_repetitions**: Number of words which repeat words spoken earlier
in the response. Responses in SEMANTIC clustering are lemmatized
before this function is called, so slight variations (dog, dogs) may
be counted as exact responses.

- **stem\_repetitions**: Number of words stems identical to words uttered
earlier in the response, according to the Porter Stemmer. For
example, 'sled' and 'sledding' have the same stem ('sled'), and
'sledding' would be counted as a stem repetition.

- **examiner\_words**: Number of words uttered by the examiner. These start
with "E\_" in .TextGrid files.

- **filled\_pauses**: Number of filled pauses uttered by the subject. These
begin with "FILLEDPAUSE\_" in the .TextGrid file.

- **word\_fragments**: Number of word fragments uttered by the subject.
These end with "-" in the .TextGrid file.

- **asides**: Words spoken by the subject that do not adhere to the test
criteria are counted as asides, i.e. words that do not start with the
appropriate letter or that do not represent an animal.

- **unique\_permissible\_words**: Number of works spoken by the subject,
less asides, stem repetitions and exact repetitions.

Measures derived from clusters/chains in the response. Each of these is
prefaced by ''COLLECTION\_'', along with the similarity measure used and
the collection type the measure was calculated over.

- **pairwise\_similarity\_score\_mean**: mean of pairwise similarity
scores. The pairwise similarity is calculated as the sum of
similarity scores for all pairwise word pairs in a response -- except
any pair composed of a word and itself -- divided by the total number
of words in an attempt. I.e., the mean similarity for all pairwise
word pairs.

- **count**: number of collections

- **size\_mean**: mean size of collections

- **size\_max**: size of largest collection

- **switch\_count**: number of changes between clusters

Measures derived from timing information in the response, along with
clusters/chains. Each of these is prefaced by ''TIMING\_'' along with
the along with the similarity measure used and the collection type the
measure was calculated over.

- **response\_vowel\_duration\_mean**: average vowel duration of all vowels
in the response.

- **response\_continuant\_duration\_mean**: average vowel duration of all
vowels in the response.

- **between\_collection\_interval\_duration\_mean**: average interval
duration separating clusters. Negative intervals (for overlapping
clusters) are counted as 0 seconds. Intervals are calculated as being
the difference between the ending time of the last word in a
collection and the start time of the first word in the subsequent
collection. Note that these intervals are not necessarily silences,
and may include asides, filled pauses, words from the examiner, etc.

- **within\_collection\_interval\_duration\_mean**: the mean time between
the end of each word in the collection and the beginning of the next
word. Note that these times do not necessarily reflect pauses, as
collection members could be separated by asides or other noises.

- **within\_collection\_vowel\_duration\_mean**: average duration of vowels
that occur within a collection

- **within\_collection\_continuant\_duration\_mean**: average duration of
continuants that occur within a collection.


This package has been tested on Mac OS X (Mavericks). In order to run
the package you must have the following installed on your machine:

0. Python 2.7

1. **pip**: pip should install with Python 2.7. If for some reason pip
is not installed, go to your terminal or commandline of choice and
enter the command below:


easy_install pip

2. **NLTK**: VFClust requires the Natural Language Toolkit (NLTK), as it
uses the NLTK lemmatizer and stemmer in parsing subject responses.
Check for more information on how to install


pip install nltk

3. **numpy**: Some of the data files are stored as numpy arrays. This will
change in future releases, but for now numpy is required to


pip install numpy

4. **gcc**: On Mac OS X, you will need to install the latest version of
Xcode compatible with your version of OS X with Command-line tools
package ( Keep in mind that you
may need to enable command-line tools in Xcode in order to be able to
use the gcc compiler. If you can't run gcc from command-line after
installing Xcode, go to the Xcode Preferences/Downloads tab and
select the "Install" button, next to "Command Line Tools."


There are two ways to install the package. VFClust is registered at, so you can install it using:


$ sudo pip install vfclust

The ``sudo`` is included because the setup process
includes compiling a file (t2p.c) and placing it in the install directory.

To install the package manually, download the .zip file from or the
.tar.gz file from Extract the file, navigate to the new
directory in the terminal, and type


$ sudo python install

You will need to have the gcc compiler installed on your system.
Installing also includes compiling a C executable for the
grapheme-to-phoneme conversion (t2p) that the phonetic clustering
package uses. If everything went okay, you should see the following
output in the console:


success S AH0 K S EH1 S

along with other output from the install process.

There are three ways to run VFClust, and therefore three tests to make sure
it's running properly. If you installed using pip, you can test the program
using some of the included example files. You should be able to type:


$ vfclust test

If you simply downloaded the package, you can navigate to the "vfclust" directory
and type


$ python test

If you are using vfclust within Python, type:


>> import vfclust
>> vfclust.test_script()

All results are the same in each case.



VFClust operations are performed on transcriptions of verbal fluency tests. These can be recorded as either CSV files or TextGrid files. For a CSV file, the first field should be the subject ID number, and each remaining field should contain a response. For example:



For a .TextGrid file, at this point the program expects two tiers, where the first includes the word strings and the second includes the phone strings. Here are the first few lines of an example file:


File type = "ooTextFile"
Object class = "TextGrid"

xmin = 0
xmax = 59.72
tiers? <exists>
size = 2
item []:
item [1]:
class = "IntervalTier"
name = "word"
xmin = 0
xmax = 59.72
intervals: size = 65
intervals [1]:
xmin = 0.00
xmax = 1.31
text = "!SIL"
intervals [2]:
xmin = 1.31
xmax = 1.83
text = "CAT"
intervals [3]:
xmin = 1.83
xmax = 2.22
text = "!SIL"
intervals [4]:
xmin = 2.22
xmax = 2.72

In both .TextGrid and .csv files, non-word noises and responses can be annotated using the following:

- !SIL = silence
- starts with E\_ = examiner word
- FILLEDPAUSE\_um or FILLEDPAUSE\_ah = filled pause
- T\_NOISE = noise
- T\_COUGH = cough
- T\_LIPSMACK = lipsmack
- T\_BREATH = breath

These special tags will be used to generate a list of counts for
Any entry that is not one of these and does not fit into the specified clustering category will be labeled as an aside.

*As a script*

After installation, you should be able to use vfclust from the command line simply by typing:


vfclust [-h] [-s SEMANTIC] [-p PHONEMIC] [-o OUTPUT_PATH] [-q]
[--similarity-file SIMILARITY_FILE] [--threshold THRESHOLD]

with the relevant parameters.

If for some reason this doesn't work, you can navigate to the directory containing the file (it should be
in the vfclust/ subdirectory of the installed package) and type:


python [-h] [-s SEMANTIC] [-p PHONEMIC] [-o OUTPUT_PATH] [-q]
[--similarity-file SIMILARITY_FILE] [--threshold THRESHOLD]

Bracketed arguments are optional, but either -s (semantic) or -p
(phonemic) must be selected. The arguments are as follows:


positional arguments:
source_file_path Full path of textgrid or csv file to parse

optional arguments:
-h, --help show this help message and exit
-s SEMANTIC Usage: -s animals If included, calculates measures for
the given category for the semantic fluency test, i.e.
animals, fruits, etc.
-p PHONEMIC Usage: -p f If included, calculates measures for the
given category for the phonemic fluency test, i.e. a,
f, s, etc.
-o OUTPUT_PATH Where to put output - default is the same directory as
the input file working directory.
-q Use to eliminate output (default is print everything
to stdout).
--similarity-file SIMILARITY_FILE
Usage: --similarity-file /path/to/similarity/file
Location of custom word similarity file. Each line
must contain two words separated by a space, followed
by a comma and the similarity number. For example,
"horse dog,1344.3969" is a valid line. If used, the
default "LSA" option is overridden. You must also
include a threshold number with --threshold X.
--threshold THRESHOLD
Usage: --threshold X, where X is a number. A custom
threshold is required when including a custom
similarity file. A custom threshold can also be set
when using semantic or phonemic clustering. In this
case, it would override the default threshold
implemented in the program.

For example, to run clustering on a phonetic verbal fluency test using the letter "f",
where the response was saved as a .csv file, type:


vfclust -p f /path/to/response/response.csv

Similarly, to run clustering on a semantic verbal fluency test using the category
"animals", where the response is recorded as a .TextGrid file, type


vfclust -s animals /path/to/response/response.TextGrid

To use a custom similarity file, type something like the following:


python --similarity-file path/to/similarity/file.txt --threshold 0.5 /path/to/response/response.TextGrid

By default, the results are printed to screen and a .csv file is created in the same
directory as the response.csv file. You can output the results to a different directory
by using the -o flag.

*As a Python package*

The functionality in the ``vfclust`` script is accessed using the ``vfclust.get_duration_measures``
method. The method inputs are as follows:


:param source_file_path: Required. Location of the .csv or .TextGrid file to be
:param output_path: Path to which to write the resultant csv file. If left None,
path will be set to the source_file_path. If set to False, no file will be
:param phonemic: The letter used for phonetic clustering. Note: should be False if
semantic clustering is being used.
:param semantic: The word category used for semantic clustering. Note: should be
False if phonetic clustering is being used.
:param quiet: Set to True if you want to suppress output to the screen during processing.
:param similarity_file (optional): When doing semantic processing, this is the path of
a file containing custom term similarity scores that will be used for clustering.
If a custom file is used, the default LSA-based clustering will not be performed.
:param threshold (optional): When doing semantic processing, this threshold is used
in conjunction with a custom similarity file. The value is used as a semantic
similarity cutoff in clustering. This argument is required if a custom similarity
file is specified. This argument can also be used to override the built-in
cluster/chain thresholds.

:return data: A dictionary of measures derived by clustering the input response.

and can be called by typing


>> import vfclust
>> results = vfclust.get_duration_measures(source_file_path = '/path/to/response/response.TextGrid',
output_path = '/output/directory/'
phonemic = 'a')

If you enter invalid arguments or both the "phonemic" and "semantic" arguments, an exception
will be raised.

*Using a custom similarity file*

You can also specify word similarities using a separate file. If this is done, words in the
response will be counted as "permissible" and as legitimate members of clusters only if they
appear somewhere in this file. VFClust will also assume all words in the file are already
tokenized, i.e. "polar bear" should be written as "polar_bear".

Each line of the file must be formatted with two words separated by a space, followed by a comma and
a number:


elk bison,114.9277
guinea_pig mouse,113.2803
panther puma,112.4150
cat skunk,112.2775
cardinal finch,111.5717
squirrel elephant,111.2780

When using a custom similarity file, you must also explicitly specify a custom threshold using the
--threshold argument.

Contributing changes


This package uses a grapheme-to-phoneme conversion (t2p) implementation
by the MBRDICO Project (

The English Open Word List is used as a basic dictionary of English

The NLTK ( WordNet 3.0 Corpus is used for lemmatizing words.


All files which are included as a part of the VFClust Phonetic
Clustering Module are provided under an Apache license, excluding:

- t2p.c in the data/t2p directory, which is provided under a GPL license

- the NLTK WordNet 3.0 corpus, which is Copyright 2006 by Princeton University.
The full text of the license is available in the corpus files.

- english\_words.txt in the data/EOWL directory, which is a
modification of the UK Advanced Cryptics Dictionary and is released
with the following licensing:

Copyright J Ross Beresford 1993-1999. All Rights Reserved. The
following restriction is placed on the use of this publication: if the
UK Advanced Cryptics Dictionary is used in a software package or
redistributed in any form, the copyright notice must be prominently
displayed and the text of this document must be included verbatim.
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