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Simulating Grammatical Encoding with Multi-Label Decision Tree Classifiers - EMCL Thesis 2018 - Data

Listed in DataCite

Data are accompanying the European Master's Thesis by Atilla Atasoy from August 2018, titled 'Simulating Grammatical Encoding with Multi-Label Decision Tree Classifiers - An Investigation into the Loci of Impairment in Turkish-Speaking Individuals with Agrammatism'.

Description

All data is provided in comma-seperated text files (txt) or comma-seperated values (csv). Six files are provided: 1.

Binary input strings (preverbal message and lemma)2. Binary output strings (surface structure)3. Semantic frame per verb root4.

Read the rest (4 more)

Appended, binary data (input and output)5. Input string template with example verb koy- 'to put'6. Output string template with example verb koy- 'to put' 7.

Raw quantitative results8. Raw qualitative results 1

Note

Due to the large number of rows (i.e., the high number of verb forms encoded), the textfiles cannot be opened with regular spreadsheet programs (e.g., Microsoft Excel). Please use software specially developed for big data (e.g., EmEditor). 1 In the raw qualitative results, duplicate headers need to be removed. This is easily accomplished in R, using the duplicated or unique function.

Links

Topics

Stated by source
Languages and literature
From keywords
Linguistics
Inferred from text
Text 75%

Related

Provenance · 1 source records, 10 field assertions
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DataCite10.6084/m9.figshare.698742514 d agoJSON v1
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concepts[field].fos:languages-and-literaturesource · DataCiteconnector:datacite@1.0.0
concepts[modality].local:modality:textenrichment · DataCitekeyword-concept-rules@1.0.0title+description (75%)
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descriptionsource · DataCiteconnector:datacite@1.0.0/data/attributes/descriptions
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publication_datesource · DataCiteconnector:datacite@1.0.0/data/attributes/dates
titlesource · DataCiteconnector:datacite@1.0.0/data/attributes/titles/0/title
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