Neighbourhood density¶

About the functions¶

Some measures of String similarity are used to calculate neighbourhood density (e.g. [Greenberg1964]; [Luce1998]; [Yao2011]), which has been shown to affect phonological processing. A phonological “neighbor” of some word X is a word that is similar in some close way to X. For example, it might differ by maximally one phone (through deletion, addition, or subsitution) from X. X’s neighborhood density, then, is the number of words that fit the criterion for being a neighbour.

Method of calculation¶

A word’s neighbourhood density is equal to the number of other words in the corpus similar to that word (or, if using token frequencies, the sum of those words’ counts). The threshold that defines whether two words are considered similar to each other can be calculated using any of the three distance metrics described in Method of calculation: Levenshtein edit distance, phonological edit distance, or [Khorsi2012] similarity. As implemented in PCT, for a query word, each other word in the corpus is checked for its similarity to the query word and then added to a list of neighbours if sufficiently similar.

For further detail about the available distance/similarity metrics, refer to Method of calculation.

Calculating neighbourhood density in the GUI¶

To start the analysis, click on “Analysis” / “Calculate neighbourhood density…” in the main menu, and then follow these steps:

1. String similarity algorithm: The first step is to choose which of the three methods of String similarity is to be used to calculate neighbourhood density. Note that the standard way of calculating density is using regular (Levenshtein) edit distance. We include the other two algorithms here as options primarily for the purpose of allowing users to explore whether they might be useful measures; we make no claims that either phonological edit distance or the Khorsi algorithm might be better than edit distance for any reason.

1. Minimal pair counts / Substitution neighbours: It is also possible to calculate neighbourhood density by using a variation of edit distance that allows for “substitutions only” (not deletions or insertions). This is particularly useful if, for example, you wish to know the number of or identity of all minimal pairs for a given word in the corpus, as minimal pairs are generally assumed to be substitution neighbours with an edit distance of 1. (Note that the substitution neighbours algorithm automatically assumes a threshold of 1; multiple substitutions are not allowed.)

2. Query type: Neighbourhood density can be calculated for one of four types of inputs:

1. One word in the corpus: The neighbourhood density of a single word can be calculated by entering that word’s orthographic representation in the query box.

2. One word not in the corpus: (Note that this will NOT add the word itself to the corpus, and will not affect any subsequent calculations. To globally add a word to the corpus itself, please see the instructions in Adding a word.) Select “Calculate for a word/nonword in the corpus,” then choose “Create word/nonword” to enter the new word and do the following:

1. Spelling: Enter the spelling for your new word / nonword using the regular input keyboard on your computer.

2. Transcription: To add in the phonetic transcription of the new word, it is best to use the provided inventory. While it is possible to type directly in to the transcription box, using the provided inventory will ensure that all characters are understood by PCT to correspond to existing characters in the corpus (with their concomitant featural interpretation). Click on “Show inventory.” (See also Edit inventory categories for more on how to set up the inventory window.) Clicking on the individual segments will add them to the transcription. Note that you do NOT need to include word boundaries at the beginning and end of the word, even when the boundary symbol is included as a member of the inventory; these will be assumed automatically by PCT.

3. Frequency and other columns: These can be left at the default. Note that entering values will NOT affect the calculation; there is no particular need to enter anything here (it is an artifact of using the same dialogue box here as in the “Add Word” function described in Adding a word).

4. Create word: To finish and return to the “String similarity” dialogue box, click on “Create word.”

3. List of words: If there is a specific list of words for which density is to be calculated (e.g., the stimuli list for an experiment), that list can be saved as a .txt file with one word per line and uploaded into PCT for analysis. The words may be written in either spelling or transcription; you simply need to specify which version the .txt file contains. Neighbourhood density will always be calculated based on transcriptions, however, so if you want to calculate ND for a word that is not in the corpus, you must supply the transcriptions. (If you provide spellings, PCT simply looks up the word’s transcription in the corpus first.) If using transcription with the Khorsi algorithm, note that all symbols in the transcription file must be symbols in the actual inventory of the corpus. E.g., if using IPA, a transcription could be [bnɪk] but not [spe7ec]. If a word in your file contains multi-character symbols, then you should use a period as a delimiter within that word; otherwise, no delimiter is necessary. E.g., if /ts/ is an affricate in your corpus, then the word /atsa/ should be written as “a.ts.a” in your file, but the word /blat/ can be written simply as “blat” in your file. Note that words in the .txt file will not be added to the corpus, nor does PCT include any of the words in the .txt file itself when calculating the neighbourhood densities of each word. E.g., if the word [bɑtɑ] is in your .txt file, and you calculate ND in the example corpus, [bɑtɑ] will be said to have two neighbours ([mɑtɑ] and [nɑtɑ]), but it will not itself count as a neighbour for either of those words (i.e., they will still each have a ND of 1). If there is a word in the .txt file that cannot be found, PCT will calculate the ND results for other words as normal and simply return “N/A” for any words it cannot handle.

Currently, the wordlist option is not eligible in conjunction with the phonological edit distance algorithm.

4. Whole corpus: Alternatively, the neighbourhood density for every word in the corpus can be calculated. This is useful, for example, if one wishes to find words that match a particular neighbourhood density. The density of each word will be added to the corpus itself, as a separate column; in the “query” box, simply enter the name of that column (the default is “Neighborhood Density”).

3. Alternative algorithm: If one is calculating the neighbourhood density for a long word in a large corpus, using edit distance with a max distance of 1, there is a linear-time algorithm that may speed up the calculation as compared to our standard algorithm. Checking this box will select this potentially faster option.

4. Collapse homophones: Before neighbourhood density is calculated, one can choose whether or not to collapse all homophones in the corpus. Collapsing homophones will mean that each set of homophonic words is counted as a single word for the purpose of calculating neighbourhood density (though no changes to the actual corpus will be implemented). For example, if the word ‘nata’ [nɑtɑ] were in the corpus, along with the words ‘mata’ [mɑtɑ], ‘mata’ [mɑtɑ], ‘sata’ [sɑtɑ], and ‘satha’ [sɑtɑ], one would expect the following behaviour (noting that ‘mata’ and ‘mata’ are both homographs and homophones, while ‘sata’ and ‘satha’ are homophones but not homographs): a. If the neighbourhood density of ‘nata’ is calculated without collapsing homophones, then it has a density of 4 ([mɑtɑ], [mɑtɑ], [sɑtɑ], and [sɑtɑ]); b. If the neighbourhood density of ‘nata’ is calculated after first collapsing homophones, then it has a density of 2 ([mɑtɑ] and [sɑtɑ]).

Note that homophones of the target word are NOT affected by this choice, and simply never count as neighbours – we assume that there must be a distance of at least 1 phone. E.g., if the neighbourhood density of ‘sata’ is calculated in the above example, it will have a density of 3 if homophones are not collapsed ([mɑtɑ], [mɑtɑ], and [nɑtɑ]; [sɑtɑ] coming from ‘satha’ does NOT count as a neighbour), while it will have a density of 2 if homophones are collapsed ([mɑtɑ] and [nɑtɑ]).

5. Tier: Neighbourhood density can be calculated from most of the available tiers in a corpus (e.g., spelling, transcription, or tiers that represent subsets of entries, such as a vowel or consonant tier). (If neighbourhood density is being calculated with phonological edit distance as the similarity metric, spelling cannot be used.) Standard neighbourhood density is calculated using edit distance on transcriptions. Note that if you are calculating ND on a list of words from a file (see 2c above), the tier must match what you put in the file (e.g., if you say the file contains spelling, PCT will force the ND calculation to be based on the spelling tier, and if you say the file contains transcription, PCT will force the ND calculations to be based on the transcription tier).

6. Pronunciation variants: If the corpus contains multiple pronunciation variants for lexical items, select what strategy should be used. For details, see Pronunciation Variants. Note that here, the only choices currently available are canonical or most-frequent forms.

7. Type vs. token frequency: If the Khorsi algorithm is selected as the string similarity metric, similarity can be calculated using either type or token frequency, as described in Khorsi (2012) Similarity Metric.

8. Distance / Similarity Threshold: A specific threshold must be set to determine what counts as a “neighbour.” If either of the edit distance metrics is selected, this should be the maximal distance that is allowed – in standard calculations of neighbourhood density, this would be 1, signifying a maximum 1-phone change from the starting word. If the Khorsi algorithm is selected, this should be the minimum similarity score that is required. Because this is not the standard way of calculating neighbourhood density, we have no recommendations for what value(s) might be good defaults here; instead, we recommend experimenting with the string similarity algorithm to determine what kinds of values are common for words that seem to count as neighbours, and working backward from that. *Note: there is an inherent ‘minimum’ of 1 as well; that is, homophones of a target word do not count as neighbours of the target word. See more in (4) (“Collapse homophones”) above.

9. Minimum Word Frequency: It is possible to set a minimum token frequency for including words in the calculation. This allows easy exclusion of rare words. To include all words in the corpus, regardless of their token frequency, set the minimum frequency to 0, or leave the field blank. Note that if a minimum frequency is set, all words below that frequency will be ignored entirely for the purposes of calculation.

10. Output file: If this option is left blank, PCT will simply return the actual neighbourhood density for each word that is calculated (i.e., the number of neighbours of each word). If a file is chosen, then the number will still be returned, but additionally, a file will be created that lists all of the actual neighbours for each word. It can be specified whether the output file should contain the orthographic representation or the transcription of each neighbour. Note that in the case of homophones that have been collapsed, the representation of the alphabetically first homophone will be the only one included.

11. Results: Once all options have been selected, click “Calculate neighborhood density.” If this is not the first calculation, and you want to add the results to a pre-existing results table, select the choice that says “add to current results table.” Otherwise, select “start new results table.” A dialogue box will open, showing a table of the results, including the word, its neighbourhood density, the string type from which neighbourhood density was calculated, what choice was made regarding pronunciation variants, whether type or token frequency was used (if applicable), the string similarity algorithm that was used, and the threshold value. If the neighbourhood density for all words in the corpus is being calculated, simply click on the “start new results table” option, and you will be returned to your corpus, where a new column has been added automatically.

12. Saving results: The results tables can each be saved to tab-delimited .txt files by selecting “Save to file” at the bottom of the window. Any output files containing actual lists of neighbours are already saved as .txt files in the location specified (see step 7). If all neighbourhood densities are calculated for a corpus, the corpus itself can be saved by going to “File” / “Export corpus as text file,” from where it can be reloaded into PCT for use in future sessions with the neighbourhood densities included.

Here’s an example of neighbourhood density being calculated on transcriptions for the entire example corpus, using edit distance with a threshold of 1, using the standard algorithm:

The corpus with all words’ densities added:

An example of calculating all the transcription neighbours for a given word in the IPHOD corpus, and saving the resulting list of neighbours to an output file:

The on-screen results table, which can be saved to a file itself:

And the saved output file listing all 45 of the neighbours of cat in the IPHOD corpus:

An example .txt file containing one word per line, that can be uploaded into PCT so that the neighbourhood density of each word is calculated:

The resulting table of neighbourhood densities for each word on the list (in the IPHOD corpus, with standard edit distance and a threshold of 1):

To return to the function dialogue box with your most recently used selections after any results table has been created, click on “Reopen function dialog.” Otherwise, the results table can be closed and you will be returned to your corpus view.

Implementing the neighbourhood density function on the command line¶

In order to perform this analysis on the command line, you must enter a command in the following format into your Terminal:

```pct_neighdens CORPUSFILE ARG2
```

…where CORPUSFILE is the name of your *.corpus file and ARG2 is either the word whose neighborhood density you wish to calculate or the name of your word list file (if calculating the neighborhood density of each word). The word list file must contain one word (specified using either spelling or transcription) on each line. You may also use command line options to change various parameters of your neighborhood density calculations. Descriptions of these arguments can be viewed by running `pct_neighdens –h` or `pct_neighdens –help`. The help text from this command is copied below, augmented with specifications of default values:

Positional arguments:

corpus_file_name

Name of corpus file

query

Name of word to query, or name of file including a list of words

Optional arguments:

-h
--help

Show this help message and exit

-c CONTEXT_TYPE
--context_type CONTEXT_TYPE

How to deal with variable pronunciations. Options are ‘Canonical’, ‘MostFrequent’, ‘SeparatedTokens’, or ‘Weighted’. See documentation for details.

-a ALGORITHM
--algorithm ALGORITHM

The algorithm used to determine distance

-d MAX_DISTANCE
--max_distance MAX_DISTANCE

Maximum edit distance from the queried word to consider a word a neighbor.

-s SEQUENCE_TYPE
--sequence_type SEQUENCE_TYPE

The name of the tier on which to calculate distance

-w COUNT_WHAT
--count_what COUNT_WHAT

If ‘type’, count neighbors in terms of their type frequency. If ‘token’, count neighbors in terms of their token frequency.

-m
--find_mutation_minpairs

This flag causes the script not to calculate neighborhood density, but rather to find minimal pairs–see documentation.

-o OUTFILE
--outfile OUTFILE

Name of output file.

EXAMPLE 1: If your corpus file is example.corpus (no pronunciation variants) and you want to calculate the neighborhood density of the word ‘nata’ using defaults for all optional arguments, you would run the following command in your terminal window:

```pct_neighdens example.corpus nata
```

EXAMPLE 2: Suppose you want to calculate the neighborhood distance of a list of words located in the file mywords.txt . Your corpus file is again example.corpus. You want to use the phonological edit distance metric, and you wish to count as a neighbor any word with a distance less than 0.75 from the query word. In addition, you want the script to produce an output file called output.txt . You would need to run the following command:

```pct_neighdens example.corpus mywords.txt -a phonological_edit_distance -d 0.75 -o output.txt
```

EXAMPLE 3: You wish to find a list of the minimal pairs of the word ‘nata’. You would need to run the following command:

```pct_neighdens example.corpus nata -m
```

Classes and functions¶

For further details about the relevant classes and functions in PCT’s source code, please refer to Neighborhood density.