Using Mylly: Frequency distributions | fi

Investigating frequency distributions in Mylly

Part I …

Mylly aims to provide tools for the computational investigation of frequency distributions that arise from observable language.


When each observed item can be put to one specific class, the classes are said to have frequency distributions. Each set of observations determines a specific frequency distribution that assigns to each class the number of items that belong to that class in that set of observations.

A family of questions arises.

  1. In a specific set of observations, what classes are frequent? How frequent are they compared to other classes? How many classes are frequent? How many classes are observed at all?
  2. How many classes are not observed at all? How different would another set of observations be?
  3. Different sets of observations show variation. Variation seems to have limits. Does an underlying population have a probability distribution that accounts for the observed frequency distributions?
  4. Is it reasonable to think that two sets of observations come from similar populations? The observations are different — are they so different that the difference matters?

Getting data

The readiest route to an annotated data set in Mylly at the time (2017-10-11) is to query Korp for a concordance from a suitable corpus. The readiest route to more than one annotated data set is to make more than one query.

Simple queries can be made in Mylly. In the following screenshot of a Mylly session, a tool has been selected to create a simple query document. [TODO: user-visible name of the tool will change] [TODO: certain filename conventions will change]

This tool does not have input files, but it has parameters: an attribute name (required) and a number of alternative values for this attribute (at least one required).

When the required parameters have values, Mylly will run the tool can be run and produce a query document that then becomes visible in the Datasets and Workflow sections of the user interface. The Visualisation section shows information about the selected query document.

Mylly can display the contents of the query document in the Visualisation section of the user interface.

The next step is to apply a search tool to the query document. Mylly search tools apply to specific corpora or families of corpora. One [currently] of them searches the Eduskunta corpus. The search is set to return at most 1000 hits, randomized to the extent allowed by Korp.

The search may take a little while. If it times out, another attempt may succeed. The response appears in the user interface as a new document in JSON form. (The random seed and the page number in the concordance are recorded in the document name and can be used to obtain further pages of the same randomized concordance.)

Selecting the query document again and running the same search tool again results in another response document.

That can wait.

Getting data in shape

There is not much [at least currently] that Mylly can do with a JSON document, other than extract the contents of a Korp concordance into other formats that it can handle. The most developed of such other formats is the representation of relations (sets of records) as documents in a slightly specialized ”tab-separated values” format.

The simplest next step is to transform a concordance into relational form. [The user-visible name of the tool will probably change.]

The result, when the tool is run, is two relations: one consisting of the ”data” (word forms and other tokens together with their various annotations), the other of the ”meta” (metadata that is properly about larger units of text than individual tokens).

There is an operation on relations (called ”join”) to join the data with the corresponding metadata. That can wait.

The Chipster platform, on which Mylly is built, has a special viewer to show TSV documents, including those TSV documents that represent relations.

Observing classes

The tokens are annotated with a word class identifier, called the ”part of speech” of the token, ”pos” for short. These identifiers can be extracted with a tool of relation algebra that is called projection. The specific tool to use in Mylly is called ”Keep selected attributes” and used here to select ”pos”.

Projection produces another relation.

The new relation has just the one field (or attribute) that was selected. Its records contain nothing that would separate different occurrences of the class identifiers, so it is just a list of the different classes that occurred at least once.

The spreadsheet viewer can show the data in the lexicographic (alphabetical) order of a selected field. The way to indicate a desire for it to do so is to click a column header — the only column header in this case. Clicking again will reverse the order.

A list of all classes is a good thing to have, but there is no way to get the counts from that alone.

Counting observations

Mylly provides a variant projection that adds the counts of the observed combinations of the selected attributes as the values of another attribute.

The tool is called ”Keep with count”, or ”Keep/count” for short.

By default the new attribute is named ”cMcount” where the stylized prefix ”cM” indicates (to some tools) that the value is a whole number (a count). Another name can be chosen.

The new relation that results from the operation is again a list of the observed combinations (observed classes when only the class is observed) — with the desired addition of the number of occurrences in the input relation.

The classes can be listed in the decreasing order of frequency by sorting on the count field (by clicking that column header in the viewer), then reversing (by clicking again).

This relation is already a frequency distribution of word class identifiers (parts of speech) in a specific concordance page from a specific corpus that was annotated with such identifiers.

Counting again

Repeating the relevant path through the exercise but starting with the other concordance that was obtained using the same query document would give another frequency distribution on the same class identifiers, assuming they all occur in the other concordance and no further identifier occur there. However, a smart thing to do this time is to choose a different name for the count attribute. (There is also a tool to rename attributes.)

This time the count attribute is called ”cMcount1” instead of the default ”cMcount”. The pos attribute is still called ”pos”.

There are the same number of class identifiers (12) in the other data set, so they may well be the same. The counts are different, as expected. The most frequent classes are the same, also as expected.

Comparing counts

The different counts can be combined into the same relation for easier ocular inspection (and further analysis). The original relation, that was derived from the first concordance, has class attribute called ”pos”, as has the new relation that was derived from the second concordance; the former has ”cMcount” where the latter has ”cMcount1”.

The ”join” tool joins the relations on the shared attributes — in this case ”pos” and nothing else.

The result has the other attributes from the records of each relation where the shared attributes match. In the case at hand, it has both frequency distributions side by side.

The parallel frequency distributions can be viewed in the order of the class frequencies in the first concordance.

The parallel frequency distributions can be viewed in the order of the class frequencies in the second concordance.

The parallel frequency distributions can also be viewed in the lexicographic (alphabetical) order of the class identifiers.

It is a start.

To be continued

There is more that can be done in Mylly already. And more soon.

Set a rather generic graphing tool to graph the parallel frequencies as points — with a smoother but …

This time the result (after correcting a typo in an attribute name — this will be made a menu) is not a relation but a graphic.

… guess it was the colour coding by the pos that ate the smoother. To be fixed. More options should also be available, like bar graphs.

With the colouring factor out of the way, the smoother appears. Look straight.

Relations (and other tables) can also be exported as Open Document Spreadsheet that a local browser should hand over to a program that can handle them.


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