Equilid is a general purpose language identification library and command line utility built with the following goals:
- Identify a broad coverage of languages
- Recognize langugage in social media, with a particular emphasis on short text
- Recognize dialectic speech from a language's speakers
- Identify code-switched text in any language pairing at least at the phrase level
- Provide whole message and per-wor
Equilid currently comes pre-trained on 70 languages (ISO 639-3 codes given):
amh ara ben bos bul cat ces cym dan deu div ell
eng est eus fas fin fra guj hat heb hin hrv hun
hye ind isl ita jpn kan kat khm kor lao lat lav
lit mal mar mkd mon msa mya nep nld nor ori pan
pol por pus ron rus sin slk slv snd spa srp swe
tam tel tgl tha tur uig ukr urd vie zho
In global settings like Twitter, this text is written by authors from diverse linguistic backgrounds, who may communicate with regional dialects or even include parallel translations in the same message to address different audiences. Such dialectal variation is frequent in all languages and even macro-dialects such as American and British English are composed of local dialects that vary across city and socioeconomic development level. Yet current systems for broad-coverage LID—trained on dozens of languages—have largely leveraged Europeancentric corpora and not taken into account demographic and dialectal variation. As a result, these systems systematically misclassify texts from populations with millions of speakers whose local speech differs from the majority dialects. Equilid aims to be a socially equitable language identification system that operates at high precision in a massively multilingual, broad-coverage domains and that supports populations speaking underrepresented dialects, multilingual messages, and other linguistic varieties.
Short summary: If you are working with text from a global environment or especially if you are working with text from a country that has dialectic language, Equilid will provide superior language identification accuracy and help you find messages from underrepresented populations.
Under the hood, Equilid uses a neural seq2seq model. It depends on three libraries:
- tensorflow 0.11.0
- numpy
- regex
Equilid may work with later versions of tensorflow but this hasn't been tested (yet).
Equilid can be installed via pip pip install equilid. However, this installs only the software and not the trained model. The trained model downloaded here [http://cs.stanford.edu/~jurgens/models/70lang.tar.gz] (559MB)
Equilid can be used as both a stand-alone file and as a python library
equilid.py [options]
Options:
-h, --help show this help message and exit
--predict launch Equilid in per-token prediction mode
--predict_file reads unlabled instances from this file
(if unspecified, STDIN is used)
--predict_output_file writes per-token predictions to this file
(if unspecified, STDOUT is used)
You can also use Equilid as a Python library:
# python
Python 2.7.12 |Anaconda custom (64-bit)| (default, Jul 2 2016, 17:42:40)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
>>> import equilid
>>> equilid.classify("This is a test.")
['eng', 'eng', 'eng', 'eng']
>>> equilid.get_langs("Esto es una prueba.")
set(['spa'])
>>> Equilid.get_langs("This is a test. Esto es una prueba.")
set(['spa', 'eng'])
Equilid's training data was drawn from multiple sources:
- JRC-Acquis
- Wikipedia (articles and talk pages)
- Debian i18n
- Qurans and Bibles
- Geographically-distributed text from different language communities
- AAVE data from Blodgett et al. (2016)
- Twitter 70 dataset
- Distinguishing Similar Languages shared task 1 and 2 data
- United Nations Declaration of Human Rights
- Watchtower magazines
- Slang websites
- Urban Dictionary
- en.mo3jam.com
- slanger.ru
- slangur.snara.is
- www.asihablamos.com
- www.dicionarioinformal.com.br
- www.straatwoordenboek.nl
- www.tubabel.com
- www.vlaamswoordenboek.be
v1.0.1:
- Fixed unicode issue
- Added model download code (untested) v1.0:
- Initial release