simplemma 0.2.0

A simple multilingual lemmatizer for Python.

Lemmatization 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 is useful in information retrieval and natural language processing.

Simplemma provides a simple and multilingual approach (currently 35 languages, see list below) 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. 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, for educational purposes or as a baseline system for lemmatization and morphological analysis.

Installation

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

pip install simplemma (or pip3 where applicable)

Usage

Word-by-word

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

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

Chaining several languages can improve coverage:

>>> langdata = simplemma.load_data('de', 'en')
>>> simplemma.lemmatize('Vaccines', langdata)
'vaccine'
>>> langdata = simplemma.load_data('it')
>>> simplemma.lemmatize('spaghettis', langdata)
'spaghettis'
>>> langdata = simplemma.load_data('it', 'fr')
>>> simplemma.lemmatize('spaghettis', langdata)
'spaghetti'
>>> simplemma.lemmatize('spaghetti', langdata)
'spaghetto'

There are cases for which a greedier algorithm is better. It is activated by default:

>>> langdata = simplemma.load_data('de')
>>> simplemma.lemmatize('angekündigten', langdata)
'ankündigen' # infinitive verb
>>> simplemma.lemmatize('angekündigten', langdata, greedy=False)
'angekündigt' # past participle

Caveats:

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

Tokenization

A simple tokenization is included:

>>> 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', '.']

The function text_lemmatizer() chains tokenization and lemmatization. It can take greedy and silent as arguments:

>>> from simplemma import text_lemmatizer
>>> langdata = simplemma.load_data('pt')
>>> text_lemmatizer('Sou o intervalo entre o que desejo ser e os outros me fizeram.', langdata)
# caveat: desejo is also a noun, should be desejar here
['ser', 'o', 'intervalo', 'entre', 'o', 'que', 'desejo', 'ser', 'e', 'o', 'outro', 'me', 'fazer', '.']

Supported languages

The following languages are available using their ISO 639-1 code:

• bg: Bulgarian, 69,680 word pairs (low coverage)

• ca: Catalan, 583,969 word pairs

• cs: Czech, 35,021 word pairs (low coverage)

• cy: Welsh, 349,638 word pairs

• da: Danish, 555,559 word pairs (alternative: lemmy)

• de: German, 623,249 word pairs (see also this list)

• en: English, 136,226 word pairs (alternative: LemmInflect)

• es: Spanish, 666,016 word pairs

• et: Estonian, 112,501 word pairs (low coverage)

• fa: Persian, 9,333 word pairs (low coverage)

• fi: Finnish, 2,096,328 word pairs (alternative: voikko)

• fr: French, 217,091 word pairs

• ga: Irish, 366,086 word pairs

• gd. Gaelic, 49,080 word pairs

• gl: Galician, 386,714 word pairs

• gv: Manx, 63,667 word pairs

• hu: Hungarian, 446,650 word pairs

• id: Indonesian, 36,461 word pairs

• it: Italian, 333,682 word pairs

• ka: Georgian, 65,938 word pairs

• la: Latin, 96,409 word pairs (low coverage)

• lb: Luxembourgish, 305,398 word pairs

• lt: Lithuanian, 247,418 word pairs

• lv: Latvian, 57,154 word pairs

• nl: Dutch, 228,123 word pairs

• pt: Portuguese, 933,730 word pairs

• ro: Romanian, 313,181 word pairs

• ru: Russian, 608,770 word pairs (alternative: pymorphy2)

• sk: Slovak, 847,383 word pairs

• sl: Slovene, 97,460 word pairs (low coverage)

• sv: Swedish, 663,984 word pairs (alternative: lemmy)

• tr: Turkish, 1,333,970 word pairs

• uk: Ukranian, 190,725 word pairs (alternative: pymorphy2)

• ur: Urdu, 28,848 word pairs

Low coverage mentions means you’d 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 available.

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

• Documentation: https://github.com/adbar/simplemma

Roadmap

• [ ] Add further lemmatization lists

• [ ] Grammatical categories as option

• [ ] Function as a meta-package?

• [ ] Integrate optional, more complex models?

Credits

The current version basically acts as a wrapper for lemmatization lists:

• Lemmatization lists by Michal Měchura (Open Database License)

• Wikinflection corpus by Eleni Metheniti (CC BY 4.0 License)

• Unimorph Project

• FreeLing project

• spaCy lookups data

This rule-based approach based on flexion and lemmatizations dictionaries is to this day an approach used in popular libraries such as spacy.

Contributions

Feel free to contribute, notably by filing issues for feedback, bug reports, or links to further lemmatization lists, rules and tests.

You can also contribute to this lemmatization list repository.

Other solutions

See lists: German-NLP and other awesome-NLP lists.

For a more complex but universal approach in Python see universal-lemmatizer.