corpkit documentation

corpkit is a Python-based tool for doing more sophisticated corpus linguistics. It exists as a graphical interface, a Python API, and a natural language interpreter. The API and interpreter are documented here.

With corpkit, you can create parsed, structured and metadata-annotated corpora, and then search them for complex lexicogrammatical patterns. Search results can be quickly edited, sorted and visualised, saved and loaded within projects, or exported to formats that can be handled by other tools. In fact, you can easily work with any dataset in CONLL U format, including the freely available, multilingual Universal Dependencies Treebanks.

Concordancing is extended to allow the user to query and display grammatical features alongside tokens. Keywording can be restricted to certain word classes or positions within the clause. If your corpus contains multiple documents or subcorpora, you can identify keywords in each, compared to the corpus as a whole.

corpkit leverages Stanford CoreNLP, NLTK and pattern for the linguistic heavy lifting, and pandas and matplotlib for storing, editing and visualising interrogation results. Multiprocessing is available via joblib, and Python 2 and 3 are both supported.

API example

Here’s a basic workflow, using a corpus of news articles published between 1987 and 2014, structured like this:


│   ├───NYT-1987-01-01-01.txt
│   ├───NYT-1987-01-02-01.txt
│   ...
│   ├───NYT-1988-01-01-01.txt
│   ├───NYT-1988-01-02-01.txt
│   ...

Below, this corpus is made into a Corpus object, parsed with Stanford CoreNLP, and interrogated for a lexicogrammatical feature. Absolute frequencies are turned into relative frequencies, and results sorted by trajectory. The edited data is then plotted.

>>> from corpkit import *
>>> from corpkit.dictionaries import processes

### parse corpus of NYT articles containing annual subcorpora
>>> unparsed = Corpus('data/NYT')
>>> parsed = unparsed.parse()

### query: nominal nsubjs that have verbal process as governor lemma
>>> crit = {F: r'^nsubj$',
...         GL: processes.verbal.lemmata,
...         P: r'^N'}

### interrogate corpus, outputting lemma forms
>>> sayers = parsed.interrogate(crit, show=L)
>>> sayers.quickview(10)

   0: official    (n=4348)
   1: expert      (n=2057)
   2: analyst     (n=1369)
   3: report      (n=1103)
   4: company     (n=1070)
   5: which       (n=1043)
   6: researcher  (n=987)
   7: study       (n=901)
   8: critic      (n=826)
   9: person      (n=802)

### get relative frequency and sort by increasing
>>> rel_say = sayers.edit('%', SELF, sort_by='increase')

### plot via matplotlib, using tex if possible
>>> rel_say.visualise('Sayers, increasing', kind='area',
...                   y_label='Percentage of all sayers')




Via pip:

$ pip install corpkit

via Git:

$ git clone
$ cd corpkit
$ python install

Parsing and interrogation of parse trees will also require Stanford CoreNLP. corpkit can download and install it for you automatically.

Graphical interface

Much of corpkit’s command line functionality is also available in the corpkit GUI. After installation, it can be started from the command line with:

$ python -m corpkit.gui

If you’re working on a project from within Python, you can open it graphically with:

>>> from corpkit import gui
>>> gui()

Alternatively, the GUI is available (alongside documentation) as a standalone OSX app here.


corpkit also has its own interpreter, a bit like the Corpus Workbench. You can open it with:

$ corpkit
# or, alternatively:
$ python -m corpkit.env

And then start working with natural language commands:

> set junglebook as corpus
> parse junglebook with outname as jb
> set jb as corpus
> search corpus for governor-lemma matching processes:verbal showing pos and lemma
> calculate result as percentage of self
> plot result as line chart with title as 'Example figure'

From the interpreter, you can enter ipython, jupyter notebook or gui to switch between interfaces, preserving the local namespace and data where possible.

Information about the syntax is available at the Overview.


If you’d like to cite corpkit, you can use:

McDonald, D. (2015). corpkit: a toolkit for corpus linguistics. Retrieved from DOI: