| 1 |
Frequency, Informativity and Word Length: Insights from Typologically Diverse Corpora
|
|
|
|
In: Entropy; Volume 24; Issue 2; Pages: 280 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Multi-word units (and tokenization more generally): a multi-dimensional and largely information-theoretic approach
|
|
|
|
In: Lexis: Journal in English Lexicology, Vol 19 (2022) (2022)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Meta-Learner for Amharic Sentiment Classification
|
|
|
|
In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
You are kidding right? The English present progressive as a stance marker in film dialogue ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
An interactive visualization of Google Books Ngrams with R and Shiny : exploring a(n) historical increase in onset strength in a(n) huge database
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
An interactive visualization of Google Books Ngrams with R and Shiny : exploring a(n) historical increase in onset strength in a(n) huge database
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
DIGITAL TECHNOLOGIES FOR GRAMMATICAL ERROR CORRECTION: DEEP LEARNING METHODS & SYNTACTIC N-GRAMS
|
|
|
|
In: Мова; No. 35 (2021) ; Мова; № 35 (2021) ; 2414-9489 ; 2307-4558 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
You are kidding right? The English present progressive as a stance marker in film dialogue
|
|
|
|
In: Lingue e Linguaggi; Volume 44(2021); 183-202 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Visualizing the development of prose styles in Horse Manuals from Early Modern English to Present-Day English
|
|
|
|
In: EISSN: 2416-5999 ; Journal of Data Mining and Digital Humanities ; https://hal.archives-ouvertes.fr/hal-02283138 ; Journal of Data Mining and Digital Humanities, Episciences.org, 2020, Special Issue Visualisations in Historical Linguistics, Special issue on Visualisations in Historical Linguistics, pp.1-33 (2020)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Frequency lists of character-level n-grams from the GOS 1.0 corpus 1.1
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Frequency lists of word-level n-grams from the GOS 1.0 corpus 1.1
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Visualizing the development of prose styles in Horse Manuals from Early Modern English to Present-Day English
|
|
|
|
In: Journal of Data Mining and Digital Humanities, Vol Special issue on Visualisations in Historical Linguistics (2020) (2020)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
An interactive visualization of Google Books Ngrams with R and Shiny: Exploring a(n) historical increase in onset strength in a(n) huge database
|
|
|
|
In: Journal of Data Mining and Digital Humanities, Vol Special issue on Visualisations in Historical Linguistics (2020) (2020)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
The necessity modals have to, must, need to and should: using n-grams to help identify common and distinct semantic and pragmatic aspects. 11.2: 220-243
|
|
|
|
In: ISSN: 1876-1933 ; EISSN: 1876-1941 ; Constructions and Frames ; https://hal.archives-ouvertes.fr/hal-02369306 ; Constructions and Frames, John Benjamins, 2019, 11, pp.220 - 243. ⟨10.1075/cf.00029.cap⟩ (2019)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
The necessity modals have to, must, need to and should: using n-grams to help identify common and distinct semantic and pragmatic aspects
|
|
|
|
In: ISSN: 1876-1933 ; EISSN: 1876-1941 ; Constructions and Frames ; https://hal.archives-ouvertes.fr/hal-02501498 ; Constructions and Frames, John Benjamins, 2019, 11 (2), pp.220-243. ⟨10.1075/cf.00029.cap⟩ (2019)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Keywords and n-grams from a textbook corpus
|
|
|
|
Abstract:
Wordlists, keywords and n-grams were extracted from a corpus of textbooks for Slovenian elementary and secondary schools. The corpus contains 4,302,857 words (5,373,268 tokens), and consists of 127 textbooks from 16 different subjects: - Biology (6 textbooks; 293,935 words), - State, society and ethics (1 textbook; 21,881 words), - Society (4 textbooks; 64,126), - Physics (5 textbooks; 185,171), - Geography (7 textbooks; 202,101 words), - Music (8 textbooks; 224,034 words), - Home Economics (3 textbooks; 33.803), - Chemistry (7 textbooks; 282,543 words), - Art (3 textbooks; 146,681), - Mathematics (23 textbooks; 764,012), - Science (5 textbooks; 226,191 words), - Science and technology (6 textbooks; 183,749 words), - Slovene language (37 textbooks; 1,437,945 words), - Environmental Education (7 textbooks; 38,645 words), - Technology (1 textbook; 24,733 words) - History (4 textbooks; 173,307 words). The lists were manually cleaned, most items not found in the reference morphological lexicon Sloleks (http://hdl.handle.net/11356/1039) were removed, which mainly consisted of conversion errors. The lists include only those words, keywords or n-grams that were found in at least 8 different subjects. Keyword lists were extracted using the Sketch Engine tool, minimum frequency was set to 5, the statistics used was average relative frequency. Minimum frequency for n-grams was 10.
|
|
Keyword:
keywords; n-grams; school; Slovenian language; textbook corpus; vocabulary; wordlist
|
|
URL: http://hdl.handle.net/11356/1215
|
|
BASE
|
|
Hide details
|
|
|
| 20 |
Dependency tree extraction tool STARK 1.0
|
|
Krsnik, Luka; Dobrovoljc, Kaja; Robnik-Šikonja, Marko. - : Centre for Language Resources and Technologies, University of Ljubljana, 2019. : Faculty of Arts, University of Ljubljana, 2019. : Faculty of Computer and Information Science, University of Ljubljana, 2019
|
|
BASE
|
|
Show details
|
|
|
|
|
