SeedLing

Building and using a seed corpus for the Human Language Project (Steven and Abney, 2010).

The SeedLing corpus on this repository includes the data from:

• ODIN: Online Database of Interlinear Text
• Omniglot: Useful foreign phrases from www.omniglot.com
• UDHR: Universal Declaration of Human Rights

The SeedLing API includes scripts to access data/information from:

• SeedLing: different data sources that forms the SeedLing corpus (odin.py, omniglot.py, udhr.py, wikipedia.py)
• ISO 639-3 : SIL ISO standards for language code (miniethnologue.py)
• WALS: Language information from World Atlas of Language Structures (miniwals.py)

FAQs:

• To use the SeedLing corpus through the python API, please follow the instructions on the Usage section.
• To download the plaintext version of the SeedLing corpus (excluding wikipedia data), click here: https://db.tt/N7hV3gwW.
• To download the wikipedia data, please follow the Getting Wikipedia section.

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Usage

To access the SeedLing from various data sources:

from seedling import udhr, omniglot, odin

# Accessing ODIN IGTs:
>>> for lang, igts in odin.igts():
>>>   for igt in igts:
>>>     print lang, igt

# Accesing Omniglot phrases
>>> for lang, sent, trans in omniglot.phrases():
>>>   print lang, sent, trans

# Accessing UDHR sentences.
>>> for lang, sent in udhr.sents():
>>>   print lang, sent

To access the SIL and WALS information:

from seedling import miniwals, miniethnologue

# Accessing SIL ISO codes.
>>> sil = miniethnologue.MiniSIL()
>>> print sil.ISO6393['eng']
{'iso6391': u'en', 'name': u'English', 'iso6392t': u'eng', 'invert': u'English', 'ismacro': False, 'scope': 'Indvidual', 'type': 'Living', 'iso6392b': u'eng'}

# Accessing WALS information
>>> wals = miniwals.MiniWALS()
>>> print wals['eng']
{u'glottocode': u'stan1293', u'name': u'English', u'family': u'Indo-European', u'longitude': u'0.0', u'sample 200': u'True', u'latitude': u'52.0', u'genus': u'Germanic', u'macroarea': u'Eurasia', u'sample 100': u'True'}

Detailed usage of the API can also be found in demo.py.

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Getting Wikipedia

There are two ways to access the Wikipedia data:

1. Plant your own Wiki
2. Access it from our cloud storage

Plant your own Wiki

We encourage SeedLing users to take part in building the Wikipedia data from the SeedLing corpus. A fruitful experience, you will find.

Please ENSURE that you have sufficient space on your harddisk (~50-70GB) and also this process of download and cleaning might take up to a week for ALL languages available in Wikipedia.

For the lazy: run the script plant_wiki.py and it would produce the desired cleaned plaintext Wikipedia data as presented in the SeedLing publication:

$ python plant_wiki.py &

For more detailed, step-by-step instructions:

• First, you have to download the Wikipedia dumps. We have used the wp-download (https://github.com/babilen/wp-download) tool when building the SeedLing corpus.
• Then, you have to extract the text from the Wikipedia dumps. We used the Wikipedia Extractor (http://medialab.di.unipi.it/wiki/Wikipedia_Extractor) to convert wikipedia dumps into textfiles.
• Finally, you can use the cleaning function in wikipedia.py to clean the Wikipedia data and assigns the ISO 639-3 language code to textfiles. The cleaning function can be called as such:

import codecs
from seedling.wikipedia import clean

extracted_wiki_dir = "/home/yourusername/path/to/extracted/wiki/"
cleaned_wiki_dir = "/home/yourusername/path/to/cleaned/wiki/"

for i in os.listdir(extracted_wiki_dir):
  dirpath, filename = os.path.split(i)
  with codecs.open(i, 'r', 'utf8') as fin, codecs.open(clean_wiki_dir+"/"+filename, 'w', 'utf8') as fout:
    fout.write(clean(fin.read()))

Please feel free to contact the colloborators in the SeedLing project if you encounter problems with getting the Wikipedia data.

Access it from our cloud storage

To be updated.

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Cite

To cite the SeedLing corpus:

Guy Emerson, Liling Tan, Susanne Fertmann, Alexis Palmer and Michaela Regneri . 2014. SeedLing: Building and using a seed corpus for the Human Language Project. In Proceedings of The use of Computational methods in the study of Endangered Languages (ComputEL) Workshop. Baltimore, USA.

in bibtex:

@InProceedings{seedling2014,
  author    = {Guy Emerson, Liling Tan, Susanne Fertmann, Alexis Palmer and Michaela Regneri},
  title     = {SeedLing: Building and using a seed corpus for the Human Language Project},
  booktitle = {Proceedings of The use of Computational methods in the study of Endangered Languages (ComputEL) Workshop},
  month     = {June},
  year      = {2014},
  address   = {Baltimore, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {},
  url       = {}
}

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References

• Steven Abney and Steven Bird. 2010. The Human Language Project: Building a universal corpus of the world’s languages. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 88–97. Association for Computational Linguistics.

• Sime Ager. Omniglot - writing systems and languages of the world. Retrieved from www.omniglot.com.

• William D Lewis and Fei Xia. 2010. Developing ODIN: A multilingual repository of annotated language data for hundreds of the world’s languages. Literary and Linguistic Computing, 25(3):303–319.

• UN General Assembly, Universal Declaration of Human Rights, 10 December 1948, 217 A (III), available at: http://www.refworld.org/docid/3ae6b3712c.html [accessed 26 April 2014]