Metadata-Version: 1.2
Name: lexicons_builder
Version: 0.1.5
Summary: lexicons_builder, a tool to create lexicons
Home-page: https://lexicons-builder.readthedocs.io
Author: GLNB
Author-email: glnb@gmail.com
License: MIT
Project-URL: Source, https://github.com/GuillaumeLNB/lexicons_builder
Project-URL: Documentation, https://lexicons-builder.readthedocs.io
Description: ================
        lexicons_builder
        ================
        
        
        The **lexicons_builder** package aims to provide a basic API to create lexicons related to specific words.
        
        
        **Key principle**: Given the input words, it will look for synonyms or neighboors in the dictionnaries or in the NLP model. For each of the new retreiven terms, it will look again for its neighboors or synonyms and so on..
        
        The general method is implemented on 3 different supports:
        
        1) Synonyms dictionnaries (See list of the dictionnaries : ref:`here <list_dictionnaries.rst>`)
        2) NLP language models
        3) `WordNet`_ (or `WOLF`_)
        
        
        Output can be text file, xlsx file, turtle file or a Graph object. See <Quickstart> section for examples.
        
        Full documentation available on `readthedocs`_
        
        
        Note
        ====
        
        Feel free to raise an issue on `GitHub`_ if something isn't working for you.
        
        
        .. _toctree: http://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html
        .. _reStructuredText: http://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html
        .. _references: http://www.sphinx-doc.org/en/stable/markup/inline.html
        .. _Python domain syntax: http://sphinx-doc.org/domains.html#the-python-domain
        .. _Sphinx: http://www.sphinx-doc.org/
        .. _Python: http://docs.python.org/
        .. _Numpy: http://docs.scipy.org/doc/numpy
        .. _SciPy: http://docs.scipy.org/doc/scipy/reference/
        .. _matplotlib: https://matplotlib.org/contents.html#
        .. _Pandas: http://pandas.pydata.org/pandas-docs/stable
        .. _Scikit-Learn: http://scikit-learn.org/stable
        .. _autodoc: http://www.sphinx-doc.org/en/stable/ext/autodoc.html
        .. _WordNet: https://wordnet.princeton.edu/
        .. _WOLF: http://alpage.inria.fr/~sagot/
        .. _readthedocs: https://lexicons-builder.readthedocs.io/en/latest/index.html
        .. _GitHub: https://github.com/GuillaumeLNB/lexicons_builder/issues
        
        
        Installation
        ------------
        
        With pip
        ~~~~~~~~
            .. code:: bash
        
                $ pip install lexicons-builder
        
        
        From source
        ~~~~~~~~~~~
        To install the module from source:
        
            .. code:: bash
        
                $ pip install git+git://github.com/GuillaumeLNB/lexicons_builder
        
        Download NLP models (optionnal)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Here's a non exhaustive list of websites where you can download NLP models manually.
        The models should be in word2vec or fasttext format.
        
        +-----------------------------------------------------------+------------------------+
        | Link                                                      | Language(s)            |
        +===========================================================+========================+
        | https://fauconnier.github.io/#data                        | French                 |
        +-----------------------------------------------------------+------------------------+
        | https://wikipedia2vec.github.io/wikipedia2vec/pretrained/ | Multilingual           |
        +-----------------------------------------------------------+------------------------+
        | http://vectors.nlpl.eu/repository/                        | Multilingual           |
        +-----------------------------------------------------------+------------------------+
        | https://github.com/alexandres/lexvec#pre-trained-vectors  | Multilingual           |
        +-----------------------------------------------------------+------------------------+
        | https://fasttext.cc/docs/en/english-vectors.html          | English / Multilingual |
        +-----------------------------------------------------------+------------------------+
        | https://github.com/mmihaltz/word2vec-GoogleNews-vectors   | English                |
        +-----------------------------------------------------------+------------------------+
        
        
        Download WOLF (French WordNet) (optionnal)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
            .. code:: bash
        
                $ # download WOLF (French wordnet if needed)
                $ wget https://gforge.inria.fr/frs/download.php/file/33496/wolf-1.0b4.xml.bz2
                $ # (and extract it)
                $ bzip2 -d wolf-1.0b4.xml.bz2
        
        QuickStart
        ------------
        
        Command line interface (CLI)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        To get words from input words through CLI, run
        
        
            .. code:: bash
        
                $ python -m lexicons_builder <words>  \
                      --lang <LANG>                 \
                      --out-file <OUTFILE>          \
                      --format <FORMAT>             \
                      --depth <DEPTH>               \
                      --nlp-model <NLP_MODEL_PATHS> \
                      --web                         \
                      --wordnet                     \
                      --wolf-path <WOLF_PATH>
        
        With:
          * ``<words>`` The word(s) we want to get synonyms from
          * ``<LANG>`` The word language (eg: *fr*, *en*, *nl*, ...)
          * ``<DEPTH>`` The depth we want to dig in the models, websites, ...
          * ``<OUTFILE>`` The file where the results will be stored
          * ``<FORMAT>`` The wanted output format (txt with indentation, ttl or xlsx)
        At least ONE of the following options is needed:
          * ``--nlp-model <NLP_MODEL_PATHS>`` The path to the nlp model(s)
          * ``--web`` Search online for synonyms
          * ``--wordnet`` Search on WordNet using nltk
          * ``--wolf-path <WOLF_PATH>`` The path to WOLF (French wordnet)
        
        **Eg:** if we want to look for related terms linked to 'eat' and 'drink' on wordnet at a depth of 2, excecute:
        
            .. code:: bash
        
                $ python -m lexicons_builder eat drink  \
                      --lang        en                  \
                      --out-file    test_en.txt         \
                      --format      txt                 \
                      --depth       1                   \
                      --wordnet
                $ Note the indentation is linked to the depth a which the word was found
                $ head test_en.txt
                  drink
                  eat
                    absorb
                    ade
                    aerophagia
                    alcohol
                    alcoholic_beverage
                    alcoholic_drink
                    banquet
                    bar_hop
                    belt_down
                    beverage
                    bi
                  ...
        
        
        
        
        Python
        ~~~~~~
        
        To get related terms interactively through Python, run
        
            .. code:: python
        
                >>> from lexicons_builder import build_lexicon
                >>> # search for related terms of 'book' and 'read' in English at depth 1 online
                >>> output = build_lexicon(["book", "read"], 'en', 1, web=True)
                ...
                >>> # we then get a graph object
                >>> # output as a list
                >>> output.to_list()
                ['PS', 'accept', 'accommodate', 'according to the rules', 'account book', 'accountability', 'accountancy', 'accountant', 'accounting', 'accounts', 'accuse', 'acquire', 'act', 'adjudge', 'admit', 'adopt', 'afl', 'agree', 'aim', "al-qur'an", 'album', 'allege', 'allocate', 'allow', 'analyse', 'analyze', 'annuaire', 'anthology', 'appear in reading', 'apply', 'appropriate', 'arrange', 'arrange for', 'arrest', 'articulate', 'ascertain' ...
                >>> # output as rdf/turtle
                >>> print(output)
                @prefix ns1: <http://taxref.mnhn.fr/lod/property/> .
                @prefix ns2: <urn:default:baseUri:#> .
                @prefix ns3: <http://www.w3.org/2004/02/skos/core#> .
                @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
        
                ns2:PS ns1:isSynonymOf ns2:root_word_uri ;
                    ns3:prefLabel "PS" ;
                    ns2:comesFrom <synonyms.com> ;
                    ns2:depth 1 .
        
                ns2:accept ns1:isSynonymOf ns2:root_word_uri ;
                    ns3:prefLabel "accept" ;
                    ns2:comesFrom <synonyms.com> ;
                    ns2:depth 1 .
                ...
        
                >>> # output to an indented file
                >>> output.to_text_file("filename.txt")
                >>> with open("filename.txt") as f:
                ...     print(f.read(1000))
                ...
                read
                book
                  PS
                  accept
                  accommodate
                  according to the rules
                  account book
                  accountability
                ...
                >>> # output to xslx file
                >>> output.to_xlsx_file("results.xlsx")
        
                >>> # full search with 2 nlp models, wordnet and on the web
                >>> # download and extract google word2vec model
                >>> # from https://github.com/mmihaltz/word2vec-GoogleNews-vectors
                >>>
                >>> # download and extract FastText models
                >>> # from https://fasttext.cc/docs/en/english-vectors.html
                >>>
                >>> nlp_models = ["GoogleNews-vectors-negative300.bin", "wiki-news-300d-1M.vec"]
                >>> output = build_lexicon(["book", "letter"], "en", 1, web=True, wordnet=True, nlp_model_paths=nlp_models)
                >>> # can take a while
                >>> len(output.to_list())
                614
        
        
        
        
        .. note::
            If the depth parameter is too high (higher than 3), the words found could seem unrelated to the root words. It can take also a long time to compute too.
        
        .. note::
            The word senses are taken equally, which means that you might get terms you would think are not related to the input word.
            Eg: looking for the word 'test' might give you words linked to Sea urchins, as a 'test' is also a type of shell https://en.wikipedia.org/wiki/Test_(biology).
        
        
        .. _GitHub: https://github.com/GuillaumeLNB/lexicons_builder/issues
Platform: UNKNOWN
