pygermanet 1.0.2
GermaNet API for Python
License: MIT
Keywords: germanet nlp wordnet

============
 pygermanet
============

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GermaNet API for Python.

Copyright (c) 23 March, 2014 Will Roberts <wildwilhelm@gmail.com>.

GermaNet_ is the German WordNet_, a machine-readable lexical semantic
resource which lists nouns, verbs and adjectives in German, along with
lexical relations which connect these words together into a semantic
network.  This library gives a Python interface to this resource.

.. _GermaNet: http://www.sfs.uni-tuebingen.de/GermaNet/
.. _WordNet: http://wordnet.princeton.edu/


Introduction
------------

Start GermaNet by connecting to the MongoDB_ database which contains
the lexical information (for setting up the MongoDB database, see the
section `Setup`_, below).  On the local machine using the default
port, this is as simple as::

    >>> from pygermanet import load_germanet
    >>> gn = load_germanet()

You can search GermaNet for synsets containing a particular lemmatised
word form using the ``synsets`` function::

    >>> gn.synsets('gehen')
    [Synset(auseinandergehen.v.3),
     Synset(funktionieren.v.1),
     Synset(funktionieren.v.2),
     Synset(gehen.v.1),
     Synset(gehen.v.4),
     Synset(gehen.v.5),
     Synset(gehen.v.6),
     Synset(gehen.v.7),
     Synset(gehen.v.9),
     Synset(gehen.v.10),
     Synset(gehen.v.11),
     Synset(gehen.v.12),
     Synset(gehen.v.13),
     Synset(gehen.v.14),
     Synset(handeln.v.1)]

To look up synsets, you must have the canonical form of the word as it
would appear in a dictionary (head word); this module calls this word
form the *lemma*.  The GermaNet instance can perform lemmatisation of
words using data derived from the `Projekt deutscher Wortschatz`_::

    >>> gn.lemmatise(u'ginge')
    [u'gehen']

.. _Projekt deutscher Wortschatz: http://wortschatz.uni-leipzig.de/

Each ``Synset`` is represented by the orthographic form, part of speech,
and sense number of its first ``Lemma``; this acts as a unique
identifier for synsets.  If you know this identifier, you can also
look up a synset in GermaNet::

    >>> funktionieren = gn.synset(u'funktionieren.v.2')
    >>> funktionieren
    Synset(funktionieren.v.2)

``Synset`` objects have data members and methods::

    >>> funktionieren.hyponyms
    [Synset(vorgehen.v.1), Synset(leerlaufen.v.2)]
    >>> gn.synset('Husky.n.1').hypernym_paths
    [[Synset(GNROOT.n.1),
      Synset(Entität.n.2),
      Synset(Objekt.n.4),
      Synset(Ding.n.2),
      Synset(Teil.n.2),
      Synset(Teilmenge.n.2),
      Synset(Gruppe.n.1),
      Synset(biologische Gruppe.n.1),
      Synset(Spezies.n.1),
      Synset(Rasse.n.1),
      Synset(Tierrasse.n.1),
      Synset(Hunderasse.n.1),
      Synset(Husky.n.1)],
     [Synset(GNROOT.n.1),
      Synset(Entität.n.2),
      Synset(kognitives Objekt.n.1),
      Synset(Kategorie.n.1),
      Synset(Art.n.1),
      Synset(Spezies.n.1),
      Synset(Rasse.n.1),
      Synset(Tierrasse.n.1),
      Synset(Hunderasse.n.1),
      Synset(Husky.n.1)],
     [Synset(GNROOT.n.1),
      Synset(Entität.n.2),
      Synset(Objekt.n.4),
      Synset(natürliches Objekt.n.1),
      Synset(Wesenheit.n.1),
      Synset(Organismus.n.1),
      Synset(höheres Lebewesen.n.1),
      Synset(Tier.n.1),
      Synset(Gewebetier.n.1),
      Synset(Chordatier.n.1),
      Synset(Wirbeltier.n.1),
      Synset(Säugetier.n.1),
      Synset(Plazentatier.n.1),
      Synset(Beutegreifer.n.1),
      Synset(Landraubtier.n.1),
      Synset(hundeartiges Landraubtier.n.1),
      Synset(Hund.n.2),
      Synset(Husky.n.1)],
     [Synset(GNROOT.n.1),
      Synset(Entität.n.2),
      Synset(Objekt.n.4),
      Synset(natürliches Objekt.n.1),
      Synset(Wesenheit.n.1),
      Synset(Organismus.n.1),
      Synset(höheres Lebewesen.n.1),
      Synset(Tier.n.1),
      Synset(Haustier.n.1),
      Synset(Hund.n.2),
      Synset(Husky.n.1)]]

Each ``Synset`` contains one or more ``Lemma`` objects::

    >>> funktionieren.lemmas
    [Lemma(funktionieren.v.2.funktionieren),
     Lemma(funktionieren.v.2.funzen),
     Lemma(funktionieren.v.2.gehen),
     Lemma(funktionieren.v.2.laufen),
     Lemma(funktionieren.v.2.arbeiten)]

A given orthographic form may be represented by multiple ``Lemma``
objects belonging to different ``Synset`` objects::

    >>> gn.lemmas('brennen')
    [Lemma(brennen.v.1.brennen),
     Lemma(verbrennen.v.1.brennen),
     Lemma(brennen.v.3.brennen),
     Lemma(brennen.v.4.brennen),
     Lemma(brennen.v.5.brennen),
     Lemma(destillieren.v.1.brennen),
     Lemma(brennen.v.7.brennen),
     Lemma(brennen.v.8.brennen)]

Semantic Similarity
-------------------

pygermanet includes several functions for calculating semantic
similarity and semantic distance, somewhat like `WN::Similarity`_.
These metrics use word frequency information estimated on the SdeWaC_
corpus and then automatically lemmatised using the TreeTagger_.

The probability of encountering an instance of a given synset *s* is
estimated over SdeWaC using the procedure described by `Resnik (1995)`_.
Briefly, for each instance of a noun in the corpus, we find the set of
synsets *S* containing a sense of that noun; each of these synsets is then
credited with a count of 1/*|S|*.  A count added to a synset is
also added to all of its hypernyms, so that count observations are
propagated up the taxonomy.  By dividing by the total number of noun
instances in the corpus, each synset is assigned a probability value;
these probabilities increase monotonically up the taxonomy, and the
root node has *p = 1*.

Using
this interface, we can replicate the results of `(Gurevych, 2005)`_
and `(Gurevych and Niederlich, 2005)`_, who collected human semantic
similarity judgements on 65 word pairs and then measured the
correlation of these judgements against similarity scores reported by
various automatic similarity metrics.  These two papers reported
Pearson's *r* of 0.715 for (Resnik, 1995), 0.738 for a normalised
distance version of `(Jiang and Conrath, 1997)`_, and 0.734 for
`(Lin, 1998)`_, with inter-annotator agreement of 0.810.

Replication of the two studies, using the gur65_ data set::

    from pygermanet import load_germanet, Synset
    from scipy.stats.stats import pearsonr
    import codecs
    import numpy as np

    GUR65_FILENAME = 'gur65.csv'

    def load_gurevych():
        gur65 = []
        with codecs.open(GUR65_FILENAME, 'r', 'latin-1') as input_file:
            for idx, line in enumerate(input_file):
                fields = line.strip().split(';')
                if idx == 0:
                    header = fields
                else:
                    # fix typo in gur65
                    fields[1] = {'Reis': 'Reise'}.get(fields[1], fields[1])
                    fields[2] = float(fields[2])
                    fields[3] = float(fields[3])
                    gur65.append(fields)
        gur65 = np.core.records.array(
            gur65,
            dtype=np.dtype({'formats': ['U30', 'U30', '<f8', '<f8'],
                            'names': header}))
        return gur65

    gur65 = load_gurevych()
    gn    = load_germanet()

    # select those words which are found in GermaNet; exclude the
    # adjective "jung"
    pred = lambda w1, w2: bool(gn.synsets(w1) and gn.synsets(w2) and
                               w1 != 'jung' and w2 != 'jung')

    print 'Semantic similarity computed on {0} of {1} word pairs'.format(
        sum([1 for word1, word2 in zip(gur65['Word1'], gur65['Word2'])
             if pred(word1, word2)]),
        len(gur65))

    sim_funcs = [('lch', Synset.sim_lch,  np.max),
                 ('res', Synset.sim_res,  np.max),
                 ('jcn', Synset.dist_jcn, np.min),
                 ('lin', Synset.sim_lin,  np.max)]

    print
    print 'metric   r'
    print '---------------'
    for sim_name, sim_func, comb_func in sim_funcs:
        scores = []
        for word1, word2, human, _hstd in gur65:
            if not pred(word1, word2):
                continue
            score = comb_func(np.array([sim_func(ss1, ss2)
                                        for ss1 in gn.synsets(word1)
                                        for ss2 in gn.synsets(word2)]))
            scores.append([score, human])
        scores = np.array(scores)
        r, _p = pearsonr(scores[:,0],scores[:,1])
        print '{0}      {1:.3f}'.format(sim_name, r)

This script outputs::

    Semantic similarity computed on 60 of 65 word pairs

    metric   r
    ---------------
    lch      0.742
    res      0.715
    jcn      -0.770
    lin      0.737

.. _`WN::Similarity`: http://wn-similarity.sourceforge.net/
.. _gur65: https://www.ukp.tu-darmstadt.de/data/semantic-relatedness/german-relatedness-datasets/
.. _TreeTagger: http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/
.. _SdeWaC: http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/sdewac.en.html
.. _`(Gurevych, 2005)`: http://atlas.tk.informatik.tu-darmstadt.de/Publications/2005/ijcnlp05.pdf
.. _`(Gurevych and Niederlich, 2005)`: http://oldsite.aclweb.org/anthology-new/I/I05/I05-7005.pdf
.. _`(Jiang and Conrath, 1997)`: http://arxiv.org/pdf/cmp-lg/9709008.pdf
.. _`(Lin, 1998)`: http://webdocs.cs.ualberta.ca/~lindek/papers/sim.pdf
.. _`Resnik (1995)`: http://arxiv.org/pdf/cmp-lg/9511007.pdf

Requirements
------------

- The GermaNet_ resource (available from the University of Tübingen)
- Python 2.7 or Python 3 (tested with Python 3.4)
- MongoDB_
- pymongo_
- future_ (for Python 2)
- `repoze.lru`_ (optional)

.. _MongoDB:    https://www.mongodb.org/
.. _pymongo:    http://api.mongodb.org/python/current/
.. _future:     http://python-future.org
.. _repoze.lru: https://pypi.python.org/pypi/repoze.lru/

Example setup::

    sudo apt-get install mongodb
    sudo pip install repoze.lru pygermanet

Setup
-----

GermaNet_ is distributed as a set of XML files, or as a PostgreSQL
database dump, neither of which is a convenient format to handle
from inside Python.  This library delegates responsibility for
handling the data to a MongoDB database.  As such, setup happens in
two steps.

1. Start a MongoDB instance running.  For example, the
   ``start_mongo.sh`` script contains::

       mkdir -p ./mongodb
       mongod --dbpath ./mongodb

2. Import GermaNet_ into the MongoDB instance.  The ``mongo_import.py``
   script needs the path to the directory that contains the GermaNet_
   XML files::

       python -m pygermanet.mongo_import ~/corpora/germanet/GN_V80/GN_V80_XML/

   This step only needs to be performed once, before you use
   pygermanet for the first time.

3. pygermanet can now be used by connecting to the running MongoDB
   instance.  Using default settings and connecting to a database on
   the local machine, this is accomplished with::

       >>> from pygermanet import load_germanet
       >>> gn = load_germanet()

License
-------

This README file and the source code in this library are licensed
under the MIT License (see source file LICENSE.txt for details).

The file ``baseforms_by_projekt_deutscher_wortschatz.txt.gz`` contains
data derived from the `Projekt deutscher Wortschatz`_; this database
is free for educational and researching purposes but not for
commercial use. For more information visit:
http://wortschatz.uni-leipzig.de/.

The file ``sdewac-gn-words.tsv.gz`` contains data derived from the
SdeWaC_ corpus, whose license and copyright status are
slightly unclear to me.  I'm releasing the file under the
`Creative Commons Attribution 4.0 International License`_.

.. _`Creative Commons Attribution 4.0 International License`: http://creativecommons.org/licenses/by/4.0/

History
-------

The NLTK_ project had an API once upon a time for interacting with
GermaNet, but this has now been removed from the current NLTK
distribution.  This API was called GermaNLTK_ and was described in
some detail in `NLTK Issue 604`_.  pygermanet shamelessly imitates the
interface of this older NLTK code, which was, in turn, based on the
standard NLTK interface to WordNet.

.. _NLTK:           http://www.nltk.org/
.. _GermaNLTK:      https://docs.google.com/document/d/1rdn0hOnJNcOBWEZgipdDfSyjJdnv_sinuAUSDSpiQns/edit?hl=en
.. _NLTK Issue 604: https://code.google.com/p/nltk/issues/detail?id=604

The GermaNLTK project had a script to push the content of the XML
files into a sqlite database; I haven't tested this code myself, and
the GermaNet database has changed over the years since GermaNLTK was
written.  This ``mongo_import.py`` script included in this library does much the
same thing, and could easily be adapted to use sqlite as a back end
instead of MongoDB.


Contributors
------------

Thanks to stefanpernes_ for his work on making a Python 3 port of
pygermanet.

.. _stefanpernes: https://github.com/stefanpernes