qdapDictionaries: Dictionaries and Word Lists for the qdap Package
5 мар. 2018 г. Fry_1000. Fry's 1000 Most Commonly Used English Words. Description. A ... txt. Top100Words. Fry's 100 Most Commonly Used English Words.
A Corpus-Based Vocabulary Selection for Grades 1-3 Thai EFL
txt includes the most frequent 1000 words of English. The second. BASEWRD2 The overlapping words included most of common nouns and basic verbs while most ...
Synthetic and real data sets for benchmarking non-cryptographic
22 мая 2019 г. These words have been drawn from the 16 most frequent English words. 1000 vectors of 512 bits synth.VARIABLE.10000x512.txt. This data set ...
CRAN - Package stylo
The default value is 1000 most frequent words. any other argument usually tokenization settings (via the parameters corpus.lang
Detection of Spelling Errors in Swedish Clinical Text-version 6 for
dictionary that contains 24396 words (English to. Swedish
VOCABULARY LOAD OF THE ENGLISH LANGUAGE
The most common crimes are probably housebreaking or burglary and robbery of one kind txt) includes the most frequent 1000 words of English. The second ( ...
Fall 2017CSE 127: Assignment 6
16 нояб. 2017 г. most common English words. 1.2 Part 2. You have another password ... txt contains the 10000 English words that people use to create their ...
How Large a Vocabulary Is Needed For Reading and Listening?
9.txt. 3053. 9.txt. 1
Food-Related Sentiment Analysis for Cantonese
txt) and 858 negative words(Negative words.txt). The resource with raw (unprocessed) Semantic differential profiles for 1000 most frequent english words.
COMMISSION OF THE EUROPEAN COMMUNITIES Brussels l2.U4
22 февр. 1990 г. Those elements which are most important for the transparency of the term ... some amendments are required to the English and Greek language ...
A Corpus-Based Vocabulary Selection for Grades 1-3 Thai EFL
As a result the Most Frequent 500-Word List for Grades 1-3 was yielded. BASEWRD1.txt includes the most frequent 1
Untitled
most frequent 1000 words of English. The second (BASEWRD2.txt) includes the 2nd. 1000 most frequent words and the third (BASEWRD3.txt) includes words not
qdapDictionaries.pdf
Mar 5 2018 Title Dictionaries and Word Lists for the 'qdap' Package. Version 1.0.7 ... Fry's 1000 Most Commonly Used English Words. Description.
VOCABULARY LOAD OF THE ENGLISH LANGUAGE
Table 2.1 Word families from the most frequent 1000 words……………………… 7 2nd 1000 most frequent words and the third (BASEWRD3.txt) includes words not in.
Introduction to the B1 Preliminary Vocabulary List
The English Vocabulary Profile shows the most common words and phrases that learners of English need to know in British or. American English. The meaning of
Package stylo
Dec 6 2020 method = "delta"
Txttool: Utilities for Text Analysis in Stata
Keywords: dm0077 txttool
Synthetic and real data sets for benchmarking non-cryptographic
May 22 2019 These words have been drawn from the 16 most frequent English words. 1000 vectors of 512 bits synth.VARIABLE.10000x512.txt.
Key Competences for Lifelong Learning
Jan 17 2018 Mathematical competence and basic competences in science and technology. ... English is the most studies foreign language
Fall 2017CSE 127: Assignment 6
Nov 16 2017 from a dictionary or some simple numbers
Package 'qdapDictionaries"
October 13, 2022
TypePackage
TitleDictionaries and Word Lists for the "qdap" PackageVersion1.0.7
Date2018-03-04
AuthorTyler Rinker
MaintainerTyler Rinker
DependsR (>= 3.0.0)
Importsmethods, utils
LazyDataTRUE
DescriptionA collection of text analysis dictionaries and word lists for use with the "qdap" package.LicenseGPL-2
RoxygenNote6.0.1
NeedsCompilationno
RepositoryCRAN
Date/Publication2018-03-05 11:29:08 UTC
Rtopics documented:
abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 action.verbs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 adverb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 amplification.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 BuckleySaltonSWL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 contractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 deamplification.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 DICTIONARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 discourse.markers.alemany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 12abbreviations
Dolch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 emoticon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Fry_1000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 function.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 GradyAugmented . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 interjections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 key.pol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 key.power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 key.strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 key.syl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 key.syn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 labMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Leveled_Dolch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 NAMES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 NAMES_LIST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 NAMES_SEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 negation.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 negative.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 OnixTxtRetToolkitSWL1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 positive.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 power.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 preposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 print.view_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 qdapDictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 strong.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 submit.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Top100Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Top200Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Top25Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
view_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
weak.words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Index24abbreviationsSmall Abbreviations Data SetDescription A dataset containing abbreviations and their qdap friendly form. Usage data(abbreviations)
Format
A data frame with 14 rows and 2 variables
action.verbs3Details
ab v.Common transcript abbre viations rep. qdap representation of those abbre viationsaction.verbsAction Word ListDescription A dataset containing a vector of action words. This is a subset of the Moby project: Moby Part-of-Speech.
Usage data(action.verbs)Format
A vector with 1569 elements
Details
From Grady Ward"s Moby project: "This second edition is a particularly thorough revision of the original Moby Part-of-Speech. Beyond the fifteen thousand new entries, many thousand more en- tries have been scrutinized for correctness and modernity. This is unquestionably the largest P-O-S list in the world. Note that the many included phrases means that parsing algorithms can nowtokenize in units larger than a single word, increasing both speed and accuracy."adverbAdverb Word ListDescription
A dataset containing a vector of adverbs words. This is a subset of the Moby project: Moby Part- of-Speech. Usage data(adverb)Format
A vector with 13398 elements
4BuckleySaltonSWL
Details
From Grady Ward"s Moby project: "This second edition is a particularly thorough revision of the original Moby Part-of-Speech. Beyond the fifteen thousand new entries, many thousand more en- tries have been scrutinized for correctness and modernity. This is unquestionably the largest P-O-S list in the world. Note that the many included phrases means that parsing algorithms can nowtokenize in units larger than a single word, increasing both speed and accuracy."amplification.wordsAmplifying WordsDescription
A dataset containing a vector of words that amplify word meaning. Usage data(amplification.words)Format
A vector with 49 elements
Details
Valence shifters are words that alter or intensify the meaning of the polarized words and include negators and amplifiers. Negators are, generally, adverbs that negate sentence meaning; for exam-ple the word like in the sentence, "I do like pie.", is given the opposite meaning in the sentence, "I do
not like pie.", now containing the negator not. Amplifiers are, generally, adverbs or adjectives that
intensify sentence meaning. Using our previous example, the sentiment of the negator altered sen-tence, "I seriously do not like pie.", is heightened with addition of the amplifier seriously. Whereas
de-amplifiers decrease the intensity of a polarized word as in the sentence "I barely like pie"; the word "barely" deamplifies the word like.BuckleySaltonSWLBuckley & Salton Stopword ListDescription A stopword list containing a character vector of stopwords. Usage data(BuckleySaltonSWL) contractions5Format
A character vector with 546 elements
Details
From Onix Text Retrieval Toolkit API Reference
: "This stopword list was built by Gerard Salton and Chris Buckley for the experimental SMART information retrieval system at Cornell University. This stopword list is generally considered to be on the larger side and so when it is used, some implementations edit it so that it is better suited for a given domain and audience while others use this stopword list as it stands." NoteReduced from the original 571 words to 546.
References
http://www.lextek.com/manuals/onix/stopwords2.htmlcontractionsContraction ConversionsDescription A dataset containing common contractions and their expanded form. Usage data(contractions)Format
A data frame with 70 rows and 2 variables
Details
contraction. The contraction w ord. e xpanded.The e xpandedform of the contraction.6DICTIONARYdeamplification.wordsDe-amplifying WordsDescription
A dataset containing a vector of words that de-amplify word meaning. Usage data(deamplification.words)Format
A vector with 13 elements
Details
Valence shifters are words that alter or intensify the meaning of the polarized words and include negators and amplifiers. Negators are, generally, adverbs that negate sentence meaning; for exam-ple the word like in the sentence, "I do like pie.", is given the opposite meaning in the sentence, "I do
not like pie.", now containing the negator not. Amplifiers are, generally, adverbs or adjectives that
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