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263 lines
12 KiB
263 lines
12 KiB
# general
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window.title=Corpus analyzer
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hyperlink.help=Help
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button.language=SL
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button.computeNgrams=Calculate
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button.cancel=Cancel
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# template
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tab.corpusTab=Corpus
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tab.filterTab=Filter
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tab.characterLevelTabNew=Characters
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tab.wordLevelTab=Word parts
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tab.oneWordAnalysisTab=Words
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tab.stringLevelTabNew2=Word sets
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# corpus tab
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label.setCorpusLocation=Set corpus location
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button.setCorpusLocation=Set location
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label.readHeaderInfo=Read info from headers
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checkBox.readHeaderInfo=
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label.chooseResultsLocation=Choose result location
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button.chooseResultsLocation=Set location
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label.selectReader=Select reader
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label.outputName=Output file name
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label.corpusTab.chooseCorpusLocationH=Select folder which contains corpus. The folder should only contain one corpus and should not contain too many files that are not part of corpus.
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label.corpusTab.readHeaderInfoH=If you select this option taxonomy will be read separately. This might take a while.
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label.corpusTab.chooseResultsLocationH=Choose result location
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label.corpusTab.selectReaderH=Select reader
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label.corpusTab.outputNameH=Output file name
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# character analysis tab
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label.stringLength=Number of letters
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label.calculateFor=Calculate for
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label.displayTaxonomy=Display taxonomies
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label.dataLimit=Data limitations
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label.msd=MSD
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label.taxonomy=Taxonomy
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label.minimalOccurrences=Min. n. occurrences
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label.minimalTaxonomy=Min. n. taxonomies
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label.solarFilters=Selected filters:
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string.lemma=lemma
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string.word=word
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label.letter.stringLengthH=Enter length of letter output.
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label.letter.calculateForH=Črkovni nizi bodo prešteti v izbranih enotah.
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label.letter.displayTaxonomyH=Izpisana bo tudi razporeditev črkovnih nizov po taksonomiji korpusa.
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label.letter.msdH=Črkovni nizi bodo prešteti samo v besedah z določeno oznako.
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label.letter.taxonomyH=Črkovni nizi bodo prešteti samo v izbranih vrstah besedil.
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label.letter.minimalOccurrencesH=Črkovni nizi z manj pojavitvami ne bodo vključeni v izpis.
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label.letter.minimalTaxonomyH=Črkovni nizi, prisotni v manj taksonomskih vejah, ne bodo vključeni v izpis.
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# word part tab
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label.alsoVisualize=Also filter
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label.lengthSearch=Search for word parts through specified length
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label.prefixLength=Prefix length
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label.suffixLength=Suffix length
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label.listSearch=Search for word parts through specified prefixes and suffixes
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label.prefixList=Prefix list
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label.suffixList=Suffix list
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label.wordPart.calculateForH=Besedni deli bodo prešteti v izbranih enotah.
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label.wordPart.alsoVisualizeH=V izpis bodo vključeni tudi izbrani podatki.
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label.wordPart.displayTaxonomyH=Izpisana bo tudi razporeditev besednih delov po taksonomiji korpusa.
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label.wordPart.prefixLengthH=
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label.wordPart.suffixLengthH=Določite dolžino (v številu črk) začetnega in/ali končnega dela besede.
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label.wordPart.prefixListH=
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label.wordPart.suffixListH=Besedne dele, ki jih želite iskati, ločite z vejico (npr. pre, raz).
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label.wordPart.msdH=Besedni deli bodo prešteti samo v besedah z določeno oznako.
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label.wordPart.taxonomyH=Besedni deli bodo prešteti samo v izbranih vrstah besedil.
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label.wordPart.minimalOccurrencesH=Enote z iskanim besednim delom, ki se pojavijo redkeje, ne bodo vključene v izpis.
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label.wordPart.minimalTaxonomyH=Enote z iskanim besednim delom, ki so prisotne v manj vejah, ne bodo vključene v izpis.
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# word tab
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label.writeMsdAtTheEnd=Razbij oblikoskladenjsko oznako
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label.word.calculateForH=Določite, kaj naj program izpisuje kot glavno enoto.
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label.word.alsoVisualizeH=V izpis bodo vključeni tudi izbrani podatki.
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label.word.displayTaxonomyH=Izpisana bo tudi razporeditev enot po taksonomiji korpusa.
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label.word.writeMsdAtTheEndH=Izpisani bodo tudi posamezni deli oblikoskladenjskih oznak.
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label.word.msdH=Preštete bodo samo besede z določeno oznako.
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label.word.taxonomyH=Besede bodo preštete samo v izbranih vrstah besedil.
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label.word.minimalOccurrencesH=Besede, ki se pojavijo redkeje, ne bodo vključene v izpis.
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label.word.minimalTaxonomyH=Besede, ki so prisotne v manj vejah, ne bodo vključene v izpis.
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# word sets tab
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label.wordSet.calculateForH=Določite, iz katerih enot bodo izpisani nizi.
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label.wordSet.alsoVisualizeH=V izpis bodo vključeni tudi izbrani podatki.
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label.wordSet.displayTaxonomyH=Izpisana bo tudi razporeditev besednih nizov po taksonomiji korpusa.
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label.wordSet.skipValueH=Vnesite največje število besed, ki se lahko pojavijo med dvema besedama v nizu.
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label.wordSet.ngramValueH=Program bo izpisal nize z izbranim številom pojavnic.
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label.wordSet.notePunctuationsH=V besedne nize bodo vključena tudi ločila.
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label.wordSet.collocabilityH=Izračunana bo tudi stopnja povezovalnosti med besedami v nizu glede na izbrane mere.
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label.wordSet.msdH=Prešteti bodo samo besedni nizi z določeno oznako.
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label.wordSet.taxonomyH=Besedni nizi bodo izpisani samo iz izbranih taksonomskih vej.
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label.wordSet.minimalOccurrencesH=Besedni nizi, ki se pojavijo redkeje, ne bodo vključeni v izpis.
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label.wordSet.minimalTaxonomyH=Besedni nizi, ki so prisotni v manj vejah, ne bodo vključeni v izpis.
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# calculate for
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calculateFor.WORD=word
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calculateFor.NORMALIZED_WORD=normalized word
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calculateFor.LEMMA=lemma
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calculateFor.MORPHOSYNTACTIC_SPECS=msd
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calculateFor.MORPHOSYNTACTIC_PROPERTY=oblikoskladenjska lastnost
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calculateFor.WORD_TYPE=word type
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calculateFor.DIST_WORDS=word
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calculateFor.DIST_LEMMAS=lemma
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# n-grams
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label.skipValue=Skip value
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label.slowSpeedWarning=* USAGE OF PREVIOUS FILTER MAY DECREASE ANALYZING SPEED
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label.ngramValue=N-gram level
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label.notePunctuations=Note punctuations
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label.collocability=Collocability
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# filtersSolar
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filter.solarRegijaL=Region
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filter.solarPredmetL=Subject
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filter.solarRazredL=Class
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filter.solarLetoL=Year
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filter.solarSolaL=School
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filter.solarVrstaBesedilaL=Text type
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filter.solarRegija=region
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filter.solarPredmet=subject
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filter.solarRazred=class
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filter.solarLeto=year
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filter.solarSola=school
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filter.solarVrstaBesedila=type
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# messages
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message.WARNING_CORPUS_NOT_FOUND=In selected directory there are no suitable corpus files.
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message.WARNING_RESULTS_DIR_NOT_VALID=You don't have correct permissions to access chosen directory.
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message.WARNING_DIFFERING_NGRAM_LEVEL_AND_FILTER_TOKENS=Selected ngram level and number of entered words do not match.
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message.WARNING_DIFFERING_NGRAM_LEVEL_AND_FILTER_TOKENS_INFO=Choose other number or modify filter.
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message.WARNING_WORD_OR_LEMMA=Choose, if you want to calculate statistics for words or lemmas.
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message.WARNING_ONLY_NUMBERS_ALLOWED=Please enter valid number.
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message.WARNING_NUMBER_TOO_BIG=Entered number is bigger than the number of taxonomies.
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message.WARNING_MISMATCHED_NGRAM_AND_TOKENS_VALUES=Number for n-gram (%d) and number of msds included (%d) must match.
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message.WARNING_MISSING_STRING_LENGTH=String length must be higher than 0. Length is set up at default value (1).
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message.WARNING_NO_TAXONOMY_FOUND=We were unable to read taxonomy from corpus files. Please select other location or different corpus.
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message.WARNING_NO_SOLAR_FILTERS_FOUND=We weren't able to read filters from corpus files. Please select other location or different corpus.
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message.ERROR_WHILE_EXECUTING=Error in program execution.
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message.ERROR_WHILE_SAVING_RESULTS_TO_CSV=Error while saving results.
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message.ERROR_NOT_ENOUGH_MEMORY=You do not have sufficient RAM for analyzing such amount of data. You can try changing filters.
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message.ERROR_NO_REGI_FILE_FOUND=Missing file \"%s\".
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message.MISSING_NGRAM_LEVEL=N-gram level
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message.MISSING_CALCULATE_FOR=Calculate for
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message.MISSING_SKIP=""
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message.MISSING_STRING_LENGTH=String length
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message.MISMATCHED_STRING_LENGTH_AND_MSD_REGEX=String length and regex filter do not match.
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message.NOTIFICATION_FOUND_X_FILES=Num. of found files: %s
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message.NOTIFICATION_CORPUS=Corpus: %s
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message.NOTIFICATION_ANALYSIS_COMPLETED=Analysis completed. Results are saved successfully.
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message.NOTIFICATION_ANALYSIS_COMPLETED_NO_RESULTS=Analysis completed, however no statistics created that would match filters.
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message.RESULTS_PATH_SET_TO_DEFAULT=Save location is set on corpus location.
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message.NOTIFICATION_ANALYSIS_CANCELED=Analysis was cancled.
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message.ONGOING_NOTIFICATION_ANALYZING_FILE_X_OF_Y=Analyzing file %d of %d (%s) - Estimated time remaining %d s
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message.CANCELING_NOTIFICATION=Canceled
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message.LABEL_CORPUS_LOCATION_NOT_SET=Corpus location is not set
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message.LABEL_RESULTS_LOCATION_NOT_SET=Result location is not set
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message.LABEL_RESULTS_CORPUS_TYPE_NOT_SET=Corpus type is not set.
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message.LABEL_SCANNING_CORPUS=Search and analysis of corpus files...
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message.LABEL_SCANNING_SINGLE_FILE_CORPUS=Input analysis
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message.COMPLETED=Completed
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#message.TOOLTIP_chooseCorpusLocationB=Select folder which contains corpus. The folder should only contain one corpus and should not contain too many files that are not part of corpus.
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#message.TOOLTIP_readHeaderInfoChB=If you select this option taxonomy will be read separately. This might take a while.
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message.TOOLTIP_readNotePunctuationsChB=Punctuations in sentences are included in analysis
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message.TOOLTIP_readDisplayTaxonomyChB=Output file will include statistics over taxonomies as well.
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windowTitles.error=Error
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windowTitles.warning=Warning
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windowTitles.confirmation=Confirmation
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# export header translations
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exportHeader.corpus=Corpus:
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exportHeader.date=Date:
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exportHeader.executionTime=Execution time:
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exportHeader.analysis=Analysis:
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exportHeader.analysis.letters=letters
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exportHeader.analysis.wordParts=word parts
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exportHeader.analysis.words=words
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exportHeader.analysis.wordSets=Word sets
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exportHeader.numberLetters=Number of letters:
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exportHeader.calculateFor=Calculate for:
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exportHeader.alsoFilter=Also filter:
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exportHeader.displayTaxonomies=Display taxonomies:
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exportHeader.ngramLevel=N-gram level:
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exportHeader.skipValue=Skip value:
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exportHeader.notePunctuations=Note punctuations:
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exportHeader.collocability=Collocability:
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exportHeader.writeMSDAtTheEnd=Write MSD at the end:
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exportHeader.prefixLength=Prefix length:
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exportHeader.suffixLength=Suffix length:
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exportHeader.prefixList=Prefix list:
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exportHeader.suffixList=Suffix list:
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exportHeader.msd=MSD:
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exportHeader.taxonomy=Taxonomy:
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exportHeader.minOccurrences=Min. n. occurrences:
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exportHeader.minTaxonomies=Min. n. taxonomies:
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exportHeader.additionalFilters=Additional filters:
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exportHeader.yes=yes
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exportHeader.no=no
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# export table header translations
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exportTable.skippedWords=Skipped words
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exportTable.lettersSmall=Letters (small letters)
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exportTable.wordsSmall=Lemma (small letters)
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exportTable.wordBeginning=Word beginning
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exportTable.wordEnding=Word ending
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exportTable.wordRest=The rest of the word
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exportTable.totalRelativeFrequency=Total relative frequency (over one million occurrences)
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exportTable.absoluteFrequency=Absolute frequency
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exportTable.percentage=Share
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exportTable.relativeFrequency=Relative frequency
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exportTable.msd=msd
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# parts
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exportTable.part.word=words:
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exportTable.part.normalizedWord=normalized words:
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exportTable.part.lemma=lemmas:
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exportTable.part.msd=msd:
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exportTable.part.msdProperty=msd property:
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exportTable.part.wordType=word type:
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exportTable.part.letterSet=letter set
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exportTable.part.word2=word
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exportTable.part.normalizedWord2=normalized word
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exportTable.part.lemma2=lemma
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exportTable.part.msd2=msd
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exportTable.part.msdProperty2=msd property
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exportTable.part.wordType2=word type
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exportTable.part.letterSet2=Share of total sum of all letter sets
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exportTable.part.letterSet3=Letter set
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exportTable.part.word3=Word
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exportTable.part.normalizedWord3=Normalized word
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exportTable.part.lemma3=Lemma
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exportTable.part.msd3=Msd
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exportTable.part.msdProperty3=Msd property
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exportTable.part.wordType3=Word type
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exportTable.part.set=set
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exportTable.part.share=Absolute share of
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exportTable.part.absoluteFrequency=Absolute frequency of
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exportTable.part.totalFound=Total sum of all
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exportTable.part.totalFoundLetters=Total sum of all found letters of
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exportTable.part.totalSumString=Total sum of
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exportTable.part.totalSumLetters=Total sum of all letters of
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# generated files names
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exportFileName.letters=letters
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exportFileName.wordParts=word-parts
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exportFileName.words=words
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exportFileName.wordSets=word-sets
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exportFileName.gram=-gram
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exportFileName.skip=-skip
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