Linguistic analysis and pipelines based on GrAF graphs

We think that GrAF graphs can play an important role in the implementation of scientific workflows in linguistics. Based on the GrAF objects that Poio API generates you might pipe the data to scientific Python libraries like networkx, numpy or scipy. The American National Corpus implemented connectors for GrAF and two linguistic frameworks. The conversion of custom file formats to GrAF through Poio API can thus act as an entry point to those pipelines and support to merge data and annotation from a wide range of heteregenous data sources for further analysis.

Search in annotation graphs: filters and filter chains

The filter class poioapi.annotationgraph.AnnotationGraphFilter can be used to search in annotation graphs in Poio API. The filter class can only be used together with the annotation graph class poioapi.annotationgraph.AnnotationGraph. The idea is that each annotation graph can contain a set of filters, that each reduce the full annotation graph to a subset. This list of filters is what we call a filter chain. Each filter consists of search terms for each of the tiers that were loaded from an input file, as described in section Data Structure Types. The search terms can be simple strings or regular expressions.

To be able to apply a filter to an annotation graph you have to load some data first. In this example we will use the example file from the Elan homepage. First, we create a new annotation graph and load the file:

import poioapi.annotationgraph

ag = poioapi.annotationgraph.AnnotationGraph()

In the next step we set the default tier hierarchy for the annotation graph. As the example file contains four root tiers with subtiers we have to choose one of the hierarchies carefully. In our case we choose the hierarchy with the root tier utterance..W-Spch that we find at index 1 of the property ag.tier_hierarchies after we loaded the file. We choose this tier hierchary to be used for all subsequent filter operations:

ag.structure_type_handler = \[1])

In our case the hierarchy ag.tier_hierarchies[1] contains the following tiers:


Now we are ready to create a filter for the data. We will filter the data with serch terms on two of the subtiers of our tier hierarchy: we will search for follow on the words tier and for the regular expression \bpro\b on the POS tier. We can look up the full names of the tiers in the above tier hierarchy. The following code creates a filter object and adds the two search terms for the two tiers:

af = poioapi.annotationgraph.AnnotationGraphFilter(ag)
af.set_filter_for_tier("words..W-Words", "follow")
af.set_filter_for_tier("part_of_speech..W-POS", r"\bpro\b")

The final step is to append the filter to the filter chain of the annotation graph:


The append operation will already start the process of graph filtering. The result is stored in the property filtered_node_ids of the annotation graph object, which is a list of root nodes where child nodes matched the search term:


You can get a visible result set by writing a filtered HTML representation of the annotation graph:

import codecs
html = ag.as_html_table(True)
f ="filtered.html", "w", "utf-8")

You can add more filters to the annotation graph by creating more filter objects and passing them to append_filter(). If you want to remove a filter you can call pop_filter(), which will remove the filter that was last added to the annotation graph object:


A convenient way to create filter objects is by passing a dictionary with tier names and search terms to the method create_filter_for_dict() of the annotation graph object. The following code will create the same filter as in the example above:

search_terms = {
    "words..W-Words": "follow",
    "part_of_speech..W-POS": r"\bpro\b"
af = ag.create_filter_for_dict(search_terms)

You can then append the filter to the filter chain. A complete script that demonstrates filters and filter chains is available on Github:

Real world examples

Counting word orders

The following example is based on the parser explained in section Spreadsheet to GrAF conversion. The whole workflow to count word order in GrAF is implemented as IPython notebook, which you can view and download here:

D3.js for visualization

The graf-python documentation contains a nice example how to visualize GrAF data with the help of the networkx library and the Javascript visualization library D3.js:

To just see the example visualization click here:

GrAF connectors

The American National Corpus implemented GrAF connectors for the Unstructured Information Management applications (Apache UIMA) fraemwork and the general architecture for text engineering (GATE) software. You can download the ANC software here: