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VFClust 0.1.1

Clustering of Verbal Fluency responses.

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 http://rxinformatics.umn.edu/downloads/VFCLUST/ryan_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

    entries

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 counted.

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 http://hetland.org/coding/python/levenshtein.py.)

  • 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.

Output

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.

Dependencies

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:

  1. Python 2.7

  2. 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
  1. NLTK: VFClust requires the Natural Language Toolkit (NLTK), as it uses the NLTK lemmatizer and stemmer in parsing subject responses. Check http://www.nltk.org for more information on how to install NLTK.
pip install nltk
  1. 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
  1. 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 (https://developer.apple.com/xcode/). 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.”

Installation

There are two ways to install the package. VFClust is registered at http://pypi.python.org/, 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 github.com or the .tar.gz file from pypi.python.org. Extract the file, navigate to the new directory in the terminal, and type

$ sudo python setup.py 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 vfclust.py test

If you are using vfclust within Python, type:

>> import vfclust
>> vfclust.test_script()

All results are the same in each case.

Deploying

Input

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:

12345,fort,friend,fry,fetch,follow,um,i,don't,know,fall,felt

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]
              source_file_path

with the relevant parameters.

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

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

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 vfclust.py --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
    analyzed.
: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
    written.
: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.

ACKNOWLEDGEMENTS

This package uses a grapheme-to-phoneme conversion (t2p) implementation by the MBRDICO Project (http://tcts.fpms.ac.be/synthesis/mbrdico/).

The English Open Word List is used as a basic dictionary of English words. http://dreamsteep.com/projects/the-english-open-word-list.html

The NLTK (http://www.nltk.org) WordNet 3.0 Corpus is used for lemmatizing words.

License

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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