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orig
stringlengths
1
87
norm
stringlengths
1
87
freq
int64
1
2.4M
docs
int64
1
237
̓
3
3
̓́ουτις.
῎ουτις▁.
1
1
̓Ασώτες
᾽Ασώτες
1
1
̓Αυτοκατάκρισις
᾽Αυτοκατάκρισις
1
1
̓Ελθει ͂ν
᾽Ελϑει῀ν
1
1
̓ταξία
᾽ταξία
1
1
̓χημοσαύη
᾽χημοσαύη
1
1
̔́Ωσπερ
῞Ωσϖερ
1
1
̔́εςί
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1
1
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1
1
!
!
22,225
175
!!
!▁!
1
1
!Aliud
!▁Aliud
1
1
!a
!▁a
5
1
"
"
1
1
#
#
7
4
&
&
21,366
185
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1
1
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12
9
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1
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701
94
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1
1
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1
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1
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1
1
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1
1
'
'
4
3
'-
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1
1
'Pfaffen
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1
1
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33,726
214
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411
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3
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(g)
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7
3
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Lexicon-DTAK-transnormer (v1.0)

This dataset is derived from dtak-transnormer-full-v1, a parallel corpus of German texts from the period between 1600 to 1899, that aligns sentences in historical spelling with their normalizations.

This dataset is a lexicon of ngram alignments between original and normalized ngrams observed in dtak-transnormer-full-v1 and their frequency. The ngram alignments in the lexicon are drawn from the sentence-level ngram alignments in dtak-transnormer-full-v1.

The dataset contains three lexicon files, one per century (1600-1699, 1700-1799, 1800-1899).

The lexicon files have the following properties for each orig-norm pair:

  • ngram_orig: ngram in original but transliterated ("ſ" -> "s", etc.) spelling
  • ngram_norm: ngram in normalized spelling that is aligned to ngram_orig
  • freq: total frequency of the pair (ngram_orig, ngram_norm) in the dataset
  • docs: document frequency of the pair, i.e. in how many documents does it occur

More on the ngram alignment: The token-level alignments produced with textalign are n:m alignments, and aim to be the best alignment between the shortest possible sequence of tokens on the layer orig_tok with the shortest possible sequence of tokens on the layer norm_tok. Therefore, most of the mappings will be 1:1 alignments, followed by 1:n/n:1 alignments.

Ngrams that contain more than a single token are joined with the special character “▁” (U+2581).

The script that created the lexicon is described here.

Usage

The lexicon can serve two functions:

  • As training data or pre-training data for a type-level normalization model
  • As a source for identifying errors in the dataset dtak-transnormer-full-v1, in order to modify it and improve the quality of the dataset. See here for more information.
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