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.. WARNING::

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Nephological Semantics
======================

Welcome! This is the current home website of the Nephological Semantics Project,
developed in the QLVL research group at KU Leuven.
You can learn more about the project :doc:`here <about>`.

One of the main products of our project is Nephosem, a Python package with functions
to create type- and token-level distributional models, both with bag-of-words and
dependency information. On this site you can find :doc:`the full reference <nephosem>`
as well as :doc:`tutorials`.

.. image:: https://zenodo.org/badge/233318567.svg

This package has been used in lexical semantics and lectometry studies within
the Nephological Semantics projects; the derived publications are listed :ref:`here <publications>`.

Specific applications
---------------------

Semasiological workflow
^^^^^^^^^^^^^^^^^^^^^^^

The semasiological workflow looks at the internal structure of individual words based on the
contexts of their occurrences. For each word, it creates multiple token-level models -vector representations
of each of its instances- combining different parameter settings (i.e. ways of defining context).
Then it selects representative models and visualizes them in an `interactive tool <https://qlvl.github.io/NephoVis/>`_.
A more or less technical explanation of the procedure is explained `here <cloudspotting.marianamontes.me/workflow.html>`_.

The Nephosem package is at the core of this workflow, but is then expanded with other tools:

* The `semasioFlow <https://github.com/montesmariana/semasioFlow>`_ Python package,
  which organizes and compacts Nephosem functions in a way specific to the semasiological workflow;
* The `semcloud <https://github.com/montesmariana/semcloud>`_ R package, which takes the output
  of semasioFlow and prepares the data for visualization, running dimensionality reduction and clustering
  and generating annotated concordances [#ann]_ .
* The `NephoVis <https://github.com/QLVL/NephoVis>`_ interactive visualization tool (see link above)
  for exhaustive, qualitative exploration of the models.
* The `Level 3 ShinyApp <https://github.com/montesmariana/Level3>`_ for deeper exploration of individual models.

To start, you can take a look at :doc:`this notebook <tutorials/createClouds>`, which shows the main steps using
semasioFlow and Nephosem, starting with a corpus in conll format (one token per line, columns for different
features) and ending with token-by-token distance matrices as well as a number of metadata registers.

Lectometric workflow
--------------------

Coming soon!

.. toctree::
   :maxdepth: 1
   :caption: Contents:

   usage
   tutorials
   Reference <nephosem>
   Repository <https://github.com/QLVL/nephosem/>
   about



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

.. rubric:: Footnotes

.. [#ann] These are not semantic annotations but model-related: context words captured by a given model are highlighted and weighting values may be included as superscript.