Convert tokens into equivalence classes defined by values of a dictionary object.
tokens_lookup( x, dictionary, levels = 1:5, valuetype = c("glob", "regex", "fixed"), case_insensitive = TRUE, capkeys = !exclusive, exclusive = TRUE, nomatch = NULL, nested_scope = c("key", "dictionary"), verbose = quanteda_options("verbose") )
| x | tokens object to which dictionary or thesaurus will be supplied |
|---|---|
| dictionary | the dictionary-class object that will be applied to
|
| levels | integers specifying the levels of entries in a hierarchical
dictionary that will be applied. The top level is 1, and subsequent levels
describe lower nesting levels. Values may be combined, even if these
levels are not contiguous, e.g. |
| valuetype | the type of pattern matching: |
| case_insensitive | logical; if |
| capkeys | if TRUE, convert dictionary keys to uppercase to distinguish them from other features |
| exclusive | if |
| nomatch | an optional character naming a new key for tokens that do not
matched to a dictionary values If |
| nested_scope | how to treat matches from different dictionary keys that
are nested. When one value is nested within another, such as "a b" being
nested within "a b c", |
| verbose | print status messages if |
Dictionary values may consist of sequences, and there are different methods of counting key matches based on values that are nested or that overlap.
When two different keys in a dictionary are nested matches of one another,
the nested_scope options provide the choice of matching each key's
values independently (the "key") option, or just counting the
longest match (the "dictionary" option). Values that are nested
within the same key are always counted as a single match. See the
last example below comparing the New York and New York Times
for these two different behaviours.
Overlapping values, such as "a b" and "b a" are
currently always considered as separate matches if they are in different
keys, or as one match if the overlap is within the same key.
Overlapped
tokens_replace
toks1 <- tokens(data_corpus_inaugural) dict1 <- dictionary(list(country = "united states", law=c("law*", "constitution"), freedom=c("free*", "libert*"))) dfm(tokens_lookup(toks1, dict1, valuetype = "glob", verbose = TRUE)) #> applying a dictionary consisting of 3 keys #> Document-feature matrix of: 59 documents, 3 features (14.69% sparse) and 4 docvars. #> features #> docs country law freedom #> 1789-Washington 2 1 6 #> 1793-Washington 0 1 0 #> 1797-Adams 3 10 6 #> 1801-Jefferson 0 6 11 #> 1805-Jefferson 1 13 6 #> 1809-Madison 2 2 5 #> [ reached max_ndoc ... 53 more documents ] dfm(tokens_lookup(toks1, dict1, valuetype = "glob", verbose = TRUE, nomatch = "NONE")) #> applying a dictionary consisting of 3 keys #> Document-feature matrix of: 59 documents, 4 features (11.02% sparse) and 4 docvars. #> features #> docs country law freedom none #> 1789-Washington 2 1 6 1526 #> 1793-Washington 0 1 0 146 #> 1797-Adams 3 10 6 2555 #> 1801-Jefferson 0 6 11 1906 #> 1805-Jefferson 1 13 6 2359 #> 1809-Madison 2 2 5 1250 #> [ reached max_ndoc ... 53 more documents ] dict2 <- dictionary(list(country = "united states", law = c("law", "constitution"), freedom = c("freedom", "liberty"))) # dfm(applyDictionary(toks1, dict2, valuetype = "fixed")) dfm(tokens_lookup(toks1, dict2, valuetype = "fixed")) #> Document-feature matrix of: 59 documents, 3 features (20.90% sparse) and 4 docvars. #> features #> docs country law freedom #> 1789-Washington 2 1 1 #> 1793-Washington 0 1 0 #> 1797-Adams 3 8 3 #> 1801-Jefferson 0 6 7 #> 1805-Jefferson 1 9 5 #> 1809-Madison 2 2 3 #> [ reached max_ndoc ... 53 more documents ] # hierarchical dictionary example txt <- c(d1 = "The United States has the Atlantic Ocean and the Pacific Ocean.", d2 = "Britain and Ireland have the Irish Sea and the English Channel.") toks2 <- tokens(txt) dict3 <- dictionary(list(US = list(Countries = c("States"), oceans = c("Atlantic", "Pacific")), Europe = list(Countries = c("Britain", "Ireland"), oceans = list(west = "Irish Sea", east = "English Channel")))) tokens_lookup(toks2, dict3, levels = 1) #> Tokens consisting of 2 documents. #> d1 : #> [1] "US" "US" "US" #> #> d2 : #> [1] "Europe" "Europe" "Europe" "Europe" #> tokens_lookup(toks2, dict3, levels = 2) #> Tokens consisting of 2 documents. #> d1 : #> [1] "Countries" "oceans" "oceans" #> #> d2 : #> [1] "Countries" "Countries" "oceans" "oceans" #> tokens_lookup(toks2, dict3, levels = 1:2) #> Tokens consisting of 2 documents. #> d1 : #> [1] "US.Countries" "US.oceans" "US.oceans" #> #> d2 : #> [1] "Europe.Countries" "Europe.Countries" "Europe.oceans" "Europe.oceans" #> tokens_lookup(toks2, dict3, levels = 3) #> Tokens consisting of 2 documents. #> d1 : #> character(0) #> #> d2 : #> [1] "west" "east" #> tokens_lookup(toks2, dict3, levels = c(1,3)) #> Tokens consisting of 2 documents. #> d1 : #> [1] "US" "US" "US" #> #> d2 : #> [1] "Europe" "Europe" "Europe.west" "Europe.east" #> tokens_lookup(toks2, dict3, levels = c(2,3)) #> Tokens consisting of 2 documents. #> d1 : #> [1] "Countries" "oceans" "oceans" #> #> d2 : #> [1] "Countries" "Countries" "oceans.west" "oceans.east" #> # show unmatched tokens tokens_lookup(toks2, dict3, nomatch = "_UNMATCHED") #> Tokens consisting of 2 documents. #> d1 : #> [1] "_UNMATCHED" "_UNMATCHED" "US.Countries" "_UNMATCHED" "_UNMATCHED" #> [6] "US.oceans" "_UNMATCHED" "_UNMATCHED" "_UNMATCHED" "US.oceans" #> [11] "_UNMATCHED" "_UNMATCHED" #> #> d2 : #> [1] "Europe.Countries" "_UNMATCHED" "Europe.Countries" #> [4] "_UNMATCHED" "_UNMATCHED" "Europe.oceans.west" #> [7] "_UNMATCHED" "_UNMATCHED" "Europe.oceans.east" #> [10] "_UNMATCHED" #> # nested matching differences dict4 <- dictionary(list(paper = "New York Times", city = "New York")) toks4 <- tokens("The New York Times is a New York paper.") tokens_lookup(toks4, dict4, nested_scope = "key", exclusive = FALSE) #> Tokens consisting of 1 document. #> text1 : #> [1] "The" "PAPER" "CITY" "is" "a" "CITY" "paper" "." #> tokens_lookup(toks4, dict4, nested_scope = "dictionary", exclusive = FALSE) #> Tokens consisting of 1 document. #> text1 : #> [1] "The" "PAPER" "is" "a" "CITY" "paper" "." #>