orig
stringlengths 1
87
| norm
stringlengths 1
87
| freq
int64 1
2.4M
| docs
int64 1
237
|
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̓
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᾽
| 3 | 3 |
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|
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| 1 | 1 |
̓Ασώτες
|
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| 1 | 1 |
̓Ελθει ͂ν
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| 1 | 1 |
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| 1 | 1 |
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| 1 | 1 |
̔́εςί
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| 1 | 1 |
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| 1 | 1 |
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!
| 22,225 | 175 |
!!
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!▁!
| 1 | 1 |
!Aliud
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| 1 | 1 |
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| 5 | 1 |
"
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|
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| 7 | 4 |
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| 12 | 9 |
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| 701 | 94 |
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|
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| 1 | 1 |
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| 1 | 1 |
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| 1 | 1 |
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| 4 | 3 |
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| 1 | 1 |
'säumest
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| 1 | 1 |
(
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| 33,726 | 214 |
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| 7 | 3 |
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.) spellingngram_norm
: ngram in normalized spelling that is aligned tongram_orig
freq
: total frequency of the pair(ngram_orig, ngram_norm)
in the datasetdocs
: 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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