lang2vec 1.1.2
A simple library for querying the URIEL typological database.

lang2vec
=======

A simple library for querying the [URIEL typological database](http://www.cs.cmu.edu/~dmortens/uriel.html),
and the learned language vectors from [Malaviya et al, 2017](https://arxiv.org/pdf/1707.09569.pdf).

Based on the [lang2vec tool](http://www.cs.cmu.edu/~dmortens/downloads/uriel_lang2vec_latest.tar.xz) by Patrick Littell.

Installation
------------
Run ``python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps lang2vec``.


Usage
-----
The library currently supports a simple operation: querying the URIEL database,
as well as the trained language vectors from [Malaviya et al, 2017](https://arxiv.org/pdf/1707.09569.pdf).
The main operation is ``get_features(languages, feature_sets, header=False, minimal=False)``, which returns a dictionary with the feature vector for every language in ``languages`` for the ``feature_sets``.


A minimal working example is:
~~~~
>>> import lang2vec.lang2vec as l2v
>>> features = l2v.get_features("eng", "geo")
>>> features["eng"]
[0.7664999961853027, 0.7924000024795532, 0.8277999758720398, 0.7214000225067139,...]
~~~~

The first argument of ``get_features()`` is either a list or a space-separated string of ISO 639-3 codes (e.g. ``["deu", "eng"]``).
Any two letter codes ISO 639-1 codes will be mapped to their corresponding ISO-639-3 codes.

~~~~
>>> features = l2v.get_features(["eng", "fra"], "geo")
>>> features["fra"]
[0.7378000020980835, 0.7682999968528748, 0.7982000112533569, 0.6941999793052673, ...]

>>> features = l2v.get_features("eng fr", "syntax_wals")
>>> features["eng"]
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...]
>>> features["fr"]
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...]
~~~~

Missing features are returned as ``'--'``. The library prints warning messages if a language is not available, e.g.
~~~~
>>> features = l2v.get_features("eng", "learned")
Note: Language eng not found in the 'learned' feature set. However, it is available in the URIEL feature sets.
>>> features["eng"][:5]
['--', '--', '--', '--', '--']
~~~~

You can list the supported languages with ``lang2vec.LANGUAGES`` or with ``lang2vec.available_languages()``.
The URIEL languages can be listed with ``lang2vec.URIEL_LANGUAGES`` or with ``lang2vec.available_uriel_languages()``.
The languages with learned vectors can be listed with ``lang2vec.LEARNED_LANGUAGES`` or with ``lang2vec.available_learned_languages()``.

The second argument is a named feature set, provided as either a string, or a list of strings, or an elementwise union A|B of two feature sets, or a concatenation A+B of two feature sets.  So "geo+syntax_wals|syntax_sswl" gives the geographical feature vector concatenated with the elementwise union of the WALS and SSWL syntax feature sets.

Note that concatenations of unions are allowed, but unions of concatenations are not. Also, the union of two feature sets is restricted to sets with similar sizes. A good rule of thumb is that two sets have similar sizes if their names start with the same prefix (`"inventory", "phonology", "syntax"`). Again, missing features (see last example) will be returned as ``'--'``.

We also provide helper functions ``fs_union()`` and ``fs_concatenation()``. They are "overloaded" so that they can receive an arbitrary number of feature set arguments or a list of feature sets. Some examples:
~~~~
>>> l2v.fs_union("syntax_wals", "syntax_sswl")
'syntax_wals|syntax_sswl'

>>> l2v.fs_union(["syntax_wals", "syntax_sswl"])
'syntax_wals|syntax_sswl'

>>> l2v.fs_concatenation( ["geo", l2v.fs_union(["syntax_wals", "syntax_sswl"])])
'geo+syntax_wals|syntax_sswl'

>>> features = l2v.get_features("eng", l2v.fs_concatenation( ["geo", l2v.fs_union(["syntax_wals", "syntax_sswl"])]))
>>> features['eng'][:5]
[0.7664999961853027, 0.7924000024795532, 0.8277999758720398, 0.7214000225067139, 0.8568999767303467]

>>> features = l2v.get_features("eng", "learned+syntax_wals")
Note: Language eng not found in the 'learned' feature set. However, it is available in the URIEL feature sets.
>>> features["eng"][:5]
['--', '--', '--', '--', '--']
>>> features["eng"][512:522]
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]
~~~~

The available feature sets can be listed with ``lang2vec.FEATURE_SETS`` or with ``lang2vec.available_feature_sets()``.
We list them here too:

* Sets from feature and inventory databases:
    * "syntax_wals",
    * "phonology_wals",
    * "syntax_sswl",
    * "syntax_ethnologue",
    * "phonology_ethnologue",
    * "inventory_ethnologue",
    * "inventory_phoible_aa",
    * "inventory_phoible_gm",
    * "inventory_phoible_saphon",
    * "inventory_phoible_spa",
    * "inventory_phoible_ph",
    * "inventory_phoible_ra",
    * "inventory_phoible_upsid",

* Averages of sets:
    * "syntax_average",
    * "phonology_average",
    * "inventory_average",

* KNN predictions of feature values:
    * "syntax_knn",
    * "phonology_knn",
    * "inventory_knn",

* Membership in language families and subfamilies:
    * "fam",

* Distance from fixed points on Earth's surface
    * "geo",

* Learned vector used for typological predictions in Malaviya et al.
    * "learned",

* One-hot identity vector:
    * "id",


There are two optional arguments to ``get_features(languages, features_sets, header=False, minimal=False)``.
Setting ``header=True`` will also return the feature names in a special dictionary entry ``'CODE'``. For example:
~~~~
>>> features = l2v.get_features("eng", "syntax_wals", header=True)
>>> features['CODE'][:5]
['S_SVO', 'S_SOV', 'S_VSO', 'S_VOS', 'S_OVS']
~~~~

Setting ``minimal=True`` will suppress the columns that contain only zeros, only ones, or only nulls.

The "minimal" transformation applies after any union or concatenation.  (If it did not, sets in the same group, like the syntax_* sets, would not be the same dimensionality for comparison.) 



REFERENCES:
-----------

If you use lang2vec, please cite the following paper:

    @inproceedings{littell2017uriel,
      title={Uriel and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors},
      author={Littell, Patrick and Mortensen, David R and Lin, Ke and Kairis, Katherine and Turner, Carlisle and Levin, Lori},
      booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
      volume={2},
      pages={8--14},
      year={2017}
    }

If you use the learned vectors, please cite the following paper:

    @inproceedings{malaviya17emnlp,
        title = {Learning Language Representations for Typology Prediction},
        author = {Malaviya, Chaitanya and Neubig, Graham and Littell, Patrick},
        booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
        address = {Copenhagen, Denmark},
        month = {September},
        year = {2017}
    }


The different feature sets above are derived from many sources:

* _wals : Features derived from the World Atlas of Language Structures.
* _sswl : Features derived from Syntactic Structures of the World's Languages.
* _ethnologue : Features derived from (shallowly) parsing the prose typological descriptions in Ethnologue (Lewis et al. 2015).
* _phoible_aa : AA = Alphabets of Africa. Features derived from PHOIBLE's normalization of *Systèmes alphabétiques des langues africaines* (Hartell 1993, Chanard 2006).
* _phoible_gm : GM = Green and Moran.  Features derived from PHOIBLE's normalization of Christopher Green and Steven Moran's pan-African inventory database.
* _phoible-ph : PH = PHOIBLE.  Features derived from PHOIBLE proper, by Moran, McCloy, and Wright (2012).
* _phoible-ra : RA = Ramaswami.  Features derived from PHOIBLE's normalization of *Common Linguistic Features in Indian Languages: Phonetics* (Ramaswami 1999).
* _phoible-saphon : SAPHON = South American Phonological Inventory Database.  Features derived from PHOIBLE's normalization of SAPHON (Lev et al. 2012).
* _phoible-spa : SPA = Stanford Phonology Archive.  Features derived from PHOIBLE's normalization of SPA (Crothers et al., 1979).
* _phoible-upsid : UPSID = UCLA Phonological Segment Inventory Database.  Features derived from PHOIBLE's normalization of UPSID (Maddieson 1984, Maddieson and Precoda 1990).
* learned_ : 512-dimensional vectors learned by a neural network over data from 1017 languages, trained for typological prediction (Malaviya et al, 2017).