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A simple library for querying the URIEL typological database.
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README.md

lang2vec

A simple library for querying the URIEL typological database, and the learned language vectors from Malaviya et al, 2017.

Based on the lang2vec tool by Patrick Littell.

Installation

Run pip3 install lang2vec. Or, if you are installing from source, clone the git repo and do python3 setup.py install.

git clone https://github.com/antonisa/lang2vec
cd lang2vec
python3 setup.py install

Usage

The library currently supports two simple operations:

  1. querying the URIEL database, as well as the trained language vectors from Malaviya et al, 2017. 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.
  2. returning pre-computed distances between languages, based on some typological information. The main operation here is distance(distance, language1, language2), which returns a float distance

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,...]
>>>
>>> l2v.distance("syntactic", "eng", "fra")
0.4569

Querying URIEL

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]

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.)

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",

Retrieving pre-computed distances

Download the distances.npz file from here and put it in the lang2vec/data directory in your installation.

These are the pre-computed distances for pairs between 8070 languages. The available distances can be listed with l2v.DISTANCES, but they are limited to genetic, geographical, phonological, syntactic, featural, and inventory distance. In most cases, these correspond to the cosine distances between the corresponding feature vectors. For more information, see the paper.

The distance(dist, langs) function receives a distance (or a list of distances) and language ISO codes (or a list of codes) as arguments. For a single distance and pair of languages, it returns a float number. If more than two languages are passed as arguments, it returns a numpy array with all the pairwise distances. If more than one distances are passed as arguments, it returns a list of the corresponding outputs. Examples:

>>> l2v.distance('syntactic', 'frr', 'dan')
0.6629
>>> 
>>> l2v.distance('syntactic', ['frr', 'dan', 'deu'])
array([[0.    , 0.6629, 0.5788],
       [0.6629, 0.    , 0.4852],
       [0.5788, 0.4852, 0.    ]])
>>> 
>>> l2v.distance(['syntactic','geographic'], 'frr', 'dan', 'deu')
[array([[0.    , 0.6629, 0.5788],
       [0.6629, 0.    , 0.4852],
       [0.5788, 0.4852, 0.    ]]),
 array([[0.    , 0.0028, 0.0359],
       [0.0028, 0.    , 0.0361],
       [0.0359, 0.0361, 0.    ]])]

We also provide helper functions for each type of distance, that only need language codes (or a list of codes) as arguments:

>>> l2v.syntactic_distance('frr','dan')
0.66
>>> l2v.geographic_distance(['frr','dan'])
0.0
>>> l2v.phonological_distance('frr','dan')
0.0002
>>> l2v.genetic_distance('frr','dan')
0.625
>>> l2v.inventory_distance('frr','dan')
0.6196
>>> l2v.featural_distance('frr','dan')
1.0

For a description of each distance, refer to the URIEL and lang2vec paper.

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).
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