Metadata-Version: 2.1
Name: word2word
Version: 1.0.0
Summary: Easy-to-use word translations for 3,564 language pairs
Home-page: https://github.com/kakaobrain/word2word
Author: Kyubyong Park, Dongwoo Kim, Yo Joong Choe
Author-email: kbpark.linguist@gmail.com, kimdwkimdw@gmail.com, yjchoe33@gmail.com
License: Apache License 2.0
Description: [![image](https://img.shields.io/pypi/v/word2word.svg)](https://pypi.org/project/word2word/)
        [![image](https://img.shields.io/pypi/l/word2word.svg)](https://pypi.org/project/word2word/)
        [![image](https://img.shields.io/pypi/pyversions/word2word.svg)](https://pypi.org/project/word2word/)
        [![image](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/kimdwkimdw)
        
        # word2word
        
        Easy-to-use word translations for 3,564 language pairs.
        
        This is the official code accompanying [our LREC 2020 paper](https://arxiv.org/abs/1911.12019).
        
        ## Summary
        
        * A large collection of freely & publicly available bilingual lexicons
            **for 3,564 language pairs across 62 unique languages.** 
        * Easy-to-use Python interface for accessing top-k word translations and 
            for building a new bilingual lexicon from a custom parallel corpus.
        * Constructed using a simple approach that yields bilingual lexicons with 
            high coverage and competitive translation quality.
        
        ## Usage
        
        First, install the package using `pip`:
        ```shell script
        pip install word2word
        ```
        
        OR
        
        ```shell script
        git clone https://github.com/kakaobrain/word2word
        python setup.py install
        ```
        
        Then, in Python, download the model and retrieve top-5 word translations 
        of any given word to the desired language:
        ```python
        from word2word import Word2word
        en2fr = Word2word("en", "fr")
        print(en2fr("apple"))
        # out: ['pomme', 'pommes', 'pommier', 'tartes', 'fleurs']
        ```
        
        ![gif](./word2word.gif)
        
        ## Supported Languages
        
        We provide top-k word-to-word translations across all available pairs 
            from [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php). 
        This amounts to a total of 3,564 language pairs across 62 unique languages. 
        
        The full list is provided [here](word2word/supporting_languages.txt).
        
        ## Methodology
        
        Our approach computes top-k word translations based on 
        the co-occurrence statistics between cross-lingual word pairs in a parallel corpus.
        We additionally introduce a correction term that controls for any confounding effect
        coming from other source words within the same sentence.
        The resulting method is an efficient and scalable approach that allows us to
        construct large bilingual dictionaries from any given parallel corpus. 
        
        For more details, see the Methodology section of [our paper](https://arxiv.org/abs/1911.12019).
        
        
        ## Building a Bilingual Lexicon on a Custom Parallel Corpus
        
        The `word2word` package also provides interface for 
        building a custom bilingual lexicon using a different parallel corpus.
        Here, we show an example of building one from 
        the [Medline English-French dataset](https://drive.google.com/drive/folders/0B3UxRWA52hBjQjZmYlRZWHQ4SUE): 
        ```python
        from word2word import Word2word
        
        # custom parallel data: data/pubmed.en-fr.en, data/pubmed.en-fr.fr
        my_en2fr = Word2word.make("en", "fr", "data/pubmed.en-fr")
        # ...building...
        print(my_en2fr("mitochondrial"))
        # out: ['mitochondriale', 'mitochondriales', 'mitochondrial', 
        #       'cytopathies', 'mitochondriaux']
        ```
        
        When built from source, the bilingual lexicon can also be constructed from the command line as follows:
        ```shell script
        python make.py --lang1 en --lang2 fr --datapref data/pubmed.en-fr
        ```
        
        In both cases, the custom lexicon (saved to `datapref/` by default) can be re-loaded in Python:
        ```python
        from word2word import Word2word
        my_en2fr = Word2word.load("en", "fr", "data/pubmed.en-fr")
        # Loaded word2word custom bilingual lexicon from data/pubmed.en-fr/en-fr.pkl
        ```
        
        ### Multiprocessing
        
        In both the Python interface and the command line interface, 
        `make` uses multiprocessing with 16 CPUs by default.
        The number of CPU workers can be adjusted by setting 
        `num_workers=N` (Python) or `--num_workers N` (command line).
        
        ## References
        
        If you use word2word for research, please cite [our paper](https://arxiv.org/abs/1911.12019):
        ```bibtex
        @inproceedings{choe2020word2word,
         author = {Yo Joong Choe and Kyubyong Park and Dongwoo Kim},
         title = {word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs},
         booktitle = {Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)},
         year = {2020}
        }
        ```
        
        All of our pre-computed bilingual lexicons were constructed from the publicly available
            [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php) dataset:
        ```bibtex
        @inproceedings{lison-etal-2018-opensubtitles2018,
            title = "{O}pen{S}ubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora",
            author = {Lison, Pierre  and
              Tiedemann, J{\"o}rg  and
              Kouylekov, Milen},
            booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
            month = may,
            year = "2018",
            address = "Miyazaki, Japan",
            publisher = "European Language Resources Association (ELRA)",
            url = "https://www.aclweb.org/anthology/L18-1275",
        }
        ```
        
        ## Authors
        
        [Kyubyong Park](https://github.com/Kyubyong), 
        [Dongwoo Kim](https://github.com/kimdwkimdw), and 
        [YJ Choe](https://github.com/yjchoe)
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
