LevshinaEtAl2023 — corpus word-order data (generated)

[LNAT+23]

Auto-generated from Linglib/Data/WordOrder/Corpus/LevshinaEtAl2023.json by scripts/gen_word_order_corpus.py. Do not edit by hand — edit the JSON and re-run the generator.

The OSF datasets of the paper's corpus illustrations: the subject–object order counts of Figure 1 (Dataset1.txt), the order entropies and case–role mutual information of Figure 3 (Dataset3.txt), and the annotated Russian clauses of the register case study in section 4.1.3 (Dataset6.txt).

def Data.WordOrder.Corpus.LevshinaEtAl2023.subjectObjectCounts : List SubjectObjectCounts

The number of clauses with a common-noun subject and object in each relative order, in the online news corpora of the Leipzig Corpora Collection for 31 languages, annotated with Universal Dependencies. The dataset's misspelling Turkisn is corrected. (31 rows; Dataset1.txt, Figure 1.)

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def Data.WordOrder.Corpus.LevshinaEtAl2023.subjectObjectStatistics : List SubjectObjectStatistics

For each of 30 online news corpora, the Shannon entropy of subject–object order and the mutual information between case markers and the syntactic roles subject and object, in thousandths of a bit, as the dataset prints them rounded to three decimals. (30 rows; Dataset3.txt, Figure 3.)

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def Data.WordOrder.Corpus.LevshinaEtAl2023.clauses : List Clause

Declarative clauses with a finite verb and a nominal object from Russian spontaneous conversations, fiction, and online news, one hundred per text type, each annotated for the order of object and verb, the object's length in words, and its animacy. (300 rows; Dataset6.txt, section 4.1.3.)

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