Old English

Old English is the earliest historical form of the English language, spoken in England and southern and eastern Scotland in the early Middle Ages. It was brought to Great Britain by Anglo-Saxon settlers probably in the mid 5th century, and the first Old English literary works date from the mid-7th century. (Source: Wikipedia)

IPA Transcription

CLTK’s IPA transcriber for OE can be found in OE’s the orthophonology module.

In [1]: from cltk.phonology.old_english.orthophonology import OldEnglishOrthophonology as oe

In [2]: oe('Fæder ūre þū þe eeart on heofonum')
Out[2]: 'fæder u:re θu: θe eæɑrt on heovonum'

In [3]: oe('Hwæt! wē Gārdena in ġēardagum')
Out[3]: 'hwæt we: gɑ:rdenɑ in jæ:ɑrdɑyum'

The callable OldEnglishOrthophonology object can also return phonemes objects instead of IPA strings. The string representation of a phoneme object lists the distinctive features of the phoneme and its IPA representation.

Corpora

Use CorpusImporter() or browse the CLTK GitHub organization (anything beginning with old_english_) to discover available Old English corpora.

In [1]: from cltk.corpus.utils.importer import CorpusImporter

In [2]: corpus_importer = CorpusImporter("old_english")

In [3]: corpus_importer.list_corpora
['old_english_text_sacred_texts', 'old_english_models_cltk']

To download a corpus, use the import_corpus method. The following will download pre-trained POS models for Old English:

In [4]: corpus_importer.import_corpus('old_english_models_cltk')

Stopword Filtering

To use the CLTK’s built-in stopwords list, We use an example from Beowulf:

In [1]: from nltk.tokenize.punkt import PunktLanguageVars

In [2]: from cltk.stop.old_english.stops import STOPS_LIST

In [3]: sentence = 'þe hie ær drugon aldorlease lange hwile.'

In [4]: p = PunktLanguageVars()

In [5]: tokens = p.word_tokenize(sentence.lower())

In [6]: [w for w in tokens if not w in STOPS_LIST]
Out[6]:
['hie',
 'drugon',
 'aldorlease',
 'catilina',
 'lange',
 'hwile',
 '.']

Text Normalization

Diacritic Stripping

The Word module provides a method useful for stripping various diacritical marks

In [1]: from cltk.phonology.old_english.phonology import Word

In [2]: Word('ġelǣd').remove_diacritics()
Out[2]: 'gelæd'

ASCII Encoding

For converting to ASCII, you can call ascii_encoding

In [3]: Word('oðþæt').ascii_encoding()
Out[3]: 'odthaet'

In [4]: Word('ƿeorðunga').ascii_encoding()
Out[4]: 'weordunga'

Transliteration

Anglo-Saxon runic transliteration

You can call the runic transliteration module for converting runic script into latin characters:

In [1]: from cltk.phonology.old_english.phonology import Transliterate as t

In [2]: t.transliterate('ᚩᚠᛏ ᛋᚳᚣᛚᛞ ᛋᚳᛖᚠᛁᛝ ᛋᚳᛠᚦᛖᚾᚪ ᚦᚱᛠᛏᚢᛗ', 'Latin')
Out[2]: 'oft scyld scefin sceathena threatum'

The reverse process is also possible:

In [3]: t.transliterate('Hƿæt Ƿe Gardena in geardagum', 'Anglo-Saxon')
Out[3]: 'ᚻᚹᚫᛏ ᚹᛖ ᚷᚪᚱᛞᛖᚾᚪ ᛁᚾ ᚷᛠᚱᛞᚪᚷᚢᛗ'

Syllabification

There is a facility for using the pre-specified sonoroty hierarchy for Old English to syllabify words.

In [1]: from cltk.phonology.syllabify import Syllabifier

In [2]: s = Syllabifier(language='old_english')

In [3]: s.syllabify('geardagum')
Out [3]:['gear', 'da', 'gum']

Lemmatization

A basic lemmatizer is provided, based on a hand-built dictionary of word forms.

In [1]: import cltk.lemmatize.old_english.lemma as oe_l
In [2]: lemmatizer = oe_l.OldEnglishDictioraryLemmatizer()
In [3]: lemmatizer.lemmatize('Næs him fruma æfre, or geworden, ne nu ende cymþ ecean')
Out [3]: [('Næs', 'næs'), ('him', 'he'), ('fruma', 'fruma'), ('æfre', 'æfre'), (',', ','), ('or', 'or'), ('geworden', 'weorþan'), (',', ','), ('ne', 'ne'), ('nu', 'nu'), ('ende', 'ende'), ('cymþ', 'cuman'), ('ecean', 'ecean')]

If an input word form has multiple possible lemmatizations, the system will select the lemma that occurs most frequently in a large corpus of Old English texts. If an input word form is not found in the dictionary, then it is simply returned.

Note, hovewer, that by passing in an extra parameter best_guess=False to the lemmatize function, one gains access to the underlying dictionary. In this case, a list is returned for each token. The list will contain:

  • Nothing, if the word form is not found;
  • A single string if the form maps to a unique lemma (the usual case);
  • Multiple strings if the form maps to several lemmatas.
In [1]: lemmatizer.lemmatize('Næs him fruma æfre, or geworden, ne nu ende cymþ ecean', best_guess=False)
Out [1]: [('Næs', ['nesan', 'næs']), ('him', ['him', 'he', 'hi']), ('fruma', ['fruma']), ('æfre', ['æfre']), (',', []), ('or', []), ('geworden', ['weorþan', 'geweorþan']), (',', []), ('ne', ['ne']), ('nu', ['nu']), ('ende', ['ende']), ('cymþ', ['cuman']), ('ecean', [])]

By specifying return_frequencies=True the log of the relative frequencies of the lemmata is also returned:

..code-block:: python

In [1]: lemmatizer.lemmatize(‘Næs him fruma æfre, or geworden, ne nu ende cymþ ecean’, best_guess=False, return_frequencies=True)

Out [1]: [(‘Næs’, [(‘nesan’, -11.498420778767347), (‘næs’, -5.340383031833549)]), (‘him’, [(‘him’, -2.1288142618657147), (‘he’, -1.4098446677862744), (‘hi’, -2.3713533259849857)]), (‘fruma’, [(‘fruma’, -7.3395376954076745)]), (‘æfre’, [(‘æfre’, -4.570372796517447)]), (‘,’, []), (‘or’, []), (‘geworden’, [(‘weorþan’, -8.608049020871182), (‘geweorþan’, -9.100525505968976)]), (‘,’, []), (‘ne’, [(‘ne’, -1.9050995182359884)]), (‘nu’, [(‘nu’, -3.393566264402446)]), (‘ende’, [(‘ende’, -5.038516324389812)]), (‘cymþ’, [(‘cuman’, -5.943525084818863)]), (‘ecean’, [])]

POS tagging

You can get the POS tags of Old English texts using the CLTK’s wrapper around the NLTK tokenizer. First, download the model by importing the old_english_models_cltk corpus.

There are a number of different pre-trained models available for POS tagging of Old English. Each represents a trade-off between accuracy of tagging and speed of tagging. Listed in order of increasing accuracy (= decreasing speed), the models are:

  • Unigram
  • Trigram -> Bigram -> Unigram n-gram backoff model
  • Conditional Random Field (CRF) model
  • Perceptron model

(Bigram and trigram models are also available, but unsuitable due to low recall.)

The taggers were trained from annotated data from the The ISWOC Treebank (license: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License).

The POS tag scheme is explained here: https://proiel.github.io/handbook/developer/

Bech, Kristin and Kristine Eide. 2014. The ISWOC corpus. Department of Literature, Area Studies and European Languages, University of Oslo. http://iswoc.github.com.

Example: Tagging with the CRF tagger

The following sentence is from the beginning of Beowulf:

In [1]: from cltk.tag.pos import POSTag

In [2]: tagger = POSTag('old_english')

In [3]: sent = 'Hwæt! We Gardena in geardagum, þeodcyninga, þrym gefrunon, hu ða æþelingas ellen fremedon.'

In [4]: tagger.tag_crf(sent)

Out[4]:[('Hwæt', 'I-'), ('!', 'C-'),
('We', 'NE'), ('Gardena', 'NE'), ('in', 'R-'), ('geardagum', 'NB'), (',', 'C-'),
('þeodcyninga', 'NB'), (',', 'C-'), ('þrym', 'PY'), ('gefrunon', 'NB'),
(',', 'C-'), ('hu', 'DU'), ('ða', 'PD'), ('æþelingas', 'NB'), ('ellen', 'V-'),
('fremedon', 'V-'), ('.', 'C-')]

Swadesh

The corpus module has a class for generating a Swadesh list for Old English.

In [1]: from cltk.corpus.swadesh import Swadesh

In [2]: swadesh = Swadesh('eng_old')

In [3]: swadesh.words()[:10]
Out[3]: ['ic, iċċ, ih', 'þū', 'hē', 'wē', 'ġē', 'hīe', 'þēs, þēos, þis', 'sē, sēo, þæt', 'hēr', 'þār, þāra, þǣr, þēr']