====================================================== Simplemma: a simple multilingual lemmatizer for Python

.. image:: https://img.shields.io/pypi/v/simplemma.svg :target: https://pypi.python.org/pypi/simplemma :alt: Python package

.. image:: https://img.shields.io/pypi/l/simplemma.svg :target: https://pypi.python.org/pypi/simplemma :alt: License

.. image:: https://img.shields.io/pypi/pyversions/simplemma.svg :target: https://pypi.python.org/pypi/simplemma :alt: Python versions

.. image:: https://img.shields.io/codecov/c/github/adbar/simplemma.svg :target: https://codecov.io/gh/adbar/simplemma :alt: Code Coverage

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black :alt: Code style: black

.. image:: https://img.shields.io/badge/DOI-10.5281%2Fzenodo.4673264-brightgreen :target: https://doi.org/10.5281/zenodo.4673264 :alt: Reference DOI: 10.5281/zenodo.4673264

Purpose

Lemmatization <https://en.wikipedia.org/wiki/Lemmatisation>_ is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. Unlike stemming, lemmatization outputs word units that are still valid linguistic forms.

In modern natural language processing (NLP), this task is often indirectly tackled by more complex systems encompassing a whole processing pipeline. However, it appears that there is no straightforward way to address lemmatization in Python although this task can be crucial in fields such as information retrieval and NLP.

Simplemma provides a simple and multilingual approach to look for base forms or lemmata. It may not be as powerful as full-fledged solutions but it is generic, easy to install and straightforward to use. In particular, it does not need morphosyntactic information and can process a raw series of tokens or even a text with its built-in tokenizer. By design it should be reasonably fast and work in a large majority of cases, without being perfect.

With its comparatively small footprint it is especially useful when speed and simplicity matter, in low-resource contexts, for educational purposes, or as a baseline system for lemmatization and morphological analysis.

Currently, 38 languages are partly or fully supported (see table below).

Installation

The current library is written in pure Python with no dependencies:

pip install simplemma

• pip3 where applicable
• pip install -U simplemma for updates

Usage

Word-by-word

Simplemma is used by selecting a language of interest and then applying the data on a list of words.

.. code-block:: python

    >>> import simplemma
    # get a word
    myword = 'masks'
    # decide which language to use and apply it on a word form
    >>> simplemma.lemmatize(myword, lang='en')
    'mask'
    # grab a list of tokens
    >>> mytokens = ['Hier', 'sind', 'Vaccines']
    >>> for token in mytokens:
    >>>     simplemma.lemmatize(token, lang='de')
    'hier'
    'sein'
    'Vaccines'
    # list comprehensions can be faster
    >>> [simplemma.lemmatize(t, lang='de') for t in mytokens]
    ['hier', 'sein', 'Vaccines']


Chaining several languages can improve coverage, they are used in sequence:


.. code-block:: python

    >>> from simplemma import lemmatize
    >>> lemmatize('Vaccines', lang=('de', 'en'))
    'vaccine'
    >>> lemmatize('spaghettis', lang='it')
    'spaghettis'
    >>> lemmatize('spaghettis', lang=('it', 'fr'))
    'spaghetti'
    >>> lemmatize('spaghetti', lang=('it', 'fr'))
    'spaghetto'


There are cases in which a greedier decomposition and lemmatization algorithm is better. It is deactivated by default:

.. code-block:: python

    # same example as before, comes to this result in one step
    >>> simplemma.lemmatize('spaghettis', lang=('it', 'fr'), greedy=True)
    'spaghetto'
    # a German case
    >>> simplemma.lemmatize('angekündigten', lang='de')
    'ankündigen' # infinitive verb
    >>> simplemma.lemmatize('angekündigten', lang='de', greedy=False)
    'angekündigt' # past participle


Additional functions:

.. code-block:: python

    # same example as before, comes to this result in one step
    >>> simplemma.is_known('spaghetti', lang='it')


Tokenization

A simple tokenization function is included for convenience:

.. code-block:: python

>>> from simplemma import simple_tokenizer
>>> simple_tokenizer('Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.')
['Lorem', 'ipsum', 'dolor', 'sit', 'amet', ',', 'consectetur', 'adipiscing', 'elit', ',', 'sed', 'do', 'eiusmod', 'tempor', 'incididunt', 'ut', 'labore', 'et', 'dolore', 'magna', 'aliqua', '.']
# use iterator instead
>>> simple_tokenizer('Lorem ipsum dolor sit amet', iterate=True)

The functions text_lemmatizer() and lemma_iterator() chain tokenization and lemmatization. They can take greedy (affecting lemmatization) and silent (affecting errors and logging) as arguments:

.. code-block:: python

>>> from simplemma import text_lemmatizer
>>> text_lemmatizer('Sou o intervalo entre o que desejo ser e os outros me fizeram.', lang='pt')
# caveat: desejo is also a noun, should be desejar here
['ser', 'o', 'intervalo', 'entre', 'o', 'que', 'desejo', 'ser', 'e', 'o', 'outro', 'me', 'fazer', '.']
# same principle, returns an iterator and not a list
>>> from simplemma import lemma_iterator

Caveats

.. code-block:: python

    # don't expect too much though
    # this diminutive form isn't in the model data
    >>> simplemma.lemmatize('spaghettini', lang='it')
    'spaghettini' # should read 'spaghettino'
    # the algorithm cannot choose between valid alternatives yet
    >>> simplemma.lemmatize('son', lang='es')
    'son' # valid common name, but what about the verb form?


As the focus lies on overall coverage, some short frequent words (typically: pronouns and conjunctions) may need post-processing, this generally concerns a few dozens of tokens per language.

The current absence of morphosyntactic information is both an advantage in terms of simplicity and an impassable frontier regarding lemmatization accuracy, e.g. disambiguation between past participles and adjectives derived from verbs in Germanic and Romance languages. In most cases, ``simplemma`` often does not change such input words.

The greedy algorithm seldom produces invalid forms. It is designed to work best in the low-frequency range, notably for compound words and neologisms. Aggressive decomposition is only useful as a general approach in the case of morphologically-rich languages, where it can also act as a linguistically motivated stemmer.

Bug reports over the `issues page <https://github.com/adbar/simplemma/issues>`_ are welcome.


Supported languages
-------------------

The following languages are available using their `ISO 639-1 code <https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`_:


====== ================== =========== ===== =========================================================================
Available languages (2022-04-06)
---------------------------------------------------------------------------------------------------------------------
Code   Language           Words (10³) Acc.  Comments
====== ================== =========== ===== =========================================================================
``bg`` Bulgarian          213
``ca`` Catalan            579
``cs`` Czech              187         0.88  on UD CS-PDT
``cy`` Welsh              360
``da`` Danish             554         0.92  on UD DA-DDT, alternative: `lemmy <https://github.com/sorenlind/lemmy>`_
``de`` German             682         0.95  on UD DE-GSD, see also `German-NLP list <https://github.com/adbar/German-NLP#Lemmatization>`_
``el`` Greek              183         0.88  on UD EL-GDT
``en`` English            136         0.94  on UD EN-GUM, alternative: `LemmInflect <https://github.com/bjascob/LemmInflect>`_
``es`` Spanish            720         0.94  on UD ES-GSD
``et`` Estonian           133               low coverage
``fa`` Persian            10                low coverage, potential issues
``fi`` Finnish            2,106             alternatives: `voikko <https://voikko.puimula.org/python.html>`_ or `NLP list <https://blogs.helsinki.fi/language-technology/hi-nlp/morphology/>`_
``fr`` French             217         0.94  on UD FR-GSD
``ga`` Irish              383
``gd`` Gaelic             48
``gl`` Galician           384
``gv`` Manx               62
``hu`` Hungarian          458
``hy`` Armenian           323
``id`` Indonesian         17          0.91  on UD ID-CSUI
``it`` Italian            333         0.93  on UD IT-ISDT
``ka`` Georgian           65
``la`` Latin              850
``lb`` Luxembourgish      305
``lt`` Lithuanian         247
``lv`` Latvian            168
``mk`` Macedonian         57
``nb`` Norwegian (Bokmål) 617
``nl`` Dutch              254         0.91  on UD-NL-Alpino
``pl`` Polish             3,733       0.91	on UD-PL-PDB
``pt`` Portuguese         933         0.92  on UD-PT-GSD
``ro`` Romanian           311
``ru`` Russian            607               alternative: `pymorphy2 <https://github.com/kmike/pymorphy2/>`_
``sk`` Slovak             846         0.92  on UD SK-SNK
``sl`` Slovenian          97                low coverage
``sv`` Swedish            658               alternative: `lemmy <https://github.com/sorenlind/lemmy>`_
``tr`` Turkish            1,333       0.88  on UD-TR-Boun
``uk`` Ukrainian          190               alternative: `pymorphy2 <https://github.com/kmike/pymorphy2/>`_
====== ================== =========== ===== =========================================================================


*Low coverage* mentions means one would probably be better off with a language-specific library, but *simplemma* will work to a limited extent. Open-source alternatives for Python are referenced if possible.

The scores are calculated on `Universal Dependencies <https://universaldependencies.org/>`_ treebanks on single word tokens (including some contractions but not merged prepositions), they describe to what extent simplemma can accurately map tokens to their lemma form. They can be reproduced using the script ``udscore.py`` in the ``tests/`` folder.

This library is particularly relevant as regards the lemmatization of less frequent words. Its performance in this case is only incidentally captured by the benchmark above.


Speed
-----

Orders of magnitude given for reference only, measured on an old laptop to give a lower bound:

- Tokenization: > 1 million tokens/sec
- Lemmatization: > 250,000 words/sec

Installing the most recent Python version can improve speed.


Optional pre-compilation with ``mypyc``

1. pip3 install mypy
2. clone or download the source code from the repository
3. python3 setup.py --use-mypyc bdist_wheel
4. pip3 install dist/*.whl (where * is the compiled wheel)

Roadmap

• [-] Add further lemmatization lists
• [ ] Grammatical categories as option
• [ ] Function as a meta-package?
• [ ] Integrate optional, more complex models?

Credits

Software under MIT license, for the linguistic information databases see licenses folder.

The surface lookups (non-greedy mode) use lemmatization lists taken from various sources:

• Lemmatization lists <https://github.com/michmech/lemmatization-lists>_ by Michal Měchura (Open Database License)
• FreeLing project <https://github.com/TALP-UPC/FreeLing>_
• spaCy lookups data <https://github.com/explosion/spacy-lookups-data/tree/master/spacy_lookups_data/data>_
• Wiktionary entries parsed by the Kaikki project <https://kaikki.org/>_
• Wikinflection corpus <https://github.com/lenakmeth/Wikinflection-Corpus>_ by Eleni Metheniti (CC BY 4.0 License)
• Unimorph Project <http://unimorph.ethz.ch/languages>_

This rule-based approach based on flexion and lemmatizations dictionaries is to this day an approach used in popular libraries such as spacy <https://spacy.io/usage/adding-languages#lemmatizer>_.

Contributions

Feel free to contribute, notably by filing issues <https://github.com/adbar/simplemma/issues/>_ for feedback, bug reports, or links to further lemmatization lists, rules and tests.

You can also contribute to this lemmatization list repository <https://github.com/michmech/lemmatization-lists>_.

Other solutions

See lists: German-NLP <https://github.com/adbar/German-NLP>_ and other awesome-NLP lists <https://github.com/adbar/German-NLP#More-lists>_.

For a more complex and universal approach in Python see universal-lemmatizer <https://github.com/jmnybl/universal-lemmatizer/>_.

References

.. image:: https://img.shields.io/badge/DOI-10.5281%2Fzenodo.4673264-brightgreen :target: https://doi.org/10.5281/zenodo.4673264 :alt: Reference DOI: 10.5281/zenodo.4673264

Barbaresi A. (2021). Simplemma: a simple multilingual lemmatizer for Python. Zenodo. http://doi.org/10.5281/zenodo.4673264

This work draws from lexical analysis algorithms used in:

• Barbaresi, A., & Hein, K. (2017). Data-driven identification of German phrasal compounds <https://hal.archives-ouvertes.fr/hal-01575651/document>_. In International Conference on Text, Speech, and Dialogue Springer, pp. 192-200.
• Barbaresi, A. (2016). Bootstrapped OCR error detection for a less-resourced language variant <https://hal.archives-ouvertes.fr/hal-01371689/document>_. In 13th Conference on Natural Language Processing (KONVENS 2016), pp. 21-26.