quanteda (version 4.1.0)

tokens_lookup: Apply a dictionary to a tokens object

Description

Convert tokens into equivalence classes defined by values of a dictionary object.

Usage

tokens_lookup(
  x,
  dictionary,
  levels = 1:5,
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  capkeys = !exclusive,
  exclusive = TRUE,
  nomatch = NULL,
  append_key = FALSE,
  separator = "/",
  concatenator = concat(x),
  nested_scope = c("key", "dictionary"),
  apply_if = NULL,
  verbose = quanteda_options("verbose")
)

Arguments

x

the tokens object to which the dictionary will be applied

dictionary

the dictionary-class object that will be applied to x

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. levels = c(1:3) will collapse the second level into the first, but record the third level (if present) collapsed below the first (see examples).

valuetype

the type of pattern matching: "glob" for "glob"-style wildcard expressions; "regex" for regular expressions; or "fixed" for exact matching. See valuetype for details.

case_insensitive

logical; if TRUE, ignore case when matching a pattern or dictionary values

capkeys

if TRUE, convert dictionary keys to uppercase to distinguish them from unmatched tokens.

exclusive

if TRUE, remove all features not in dictionary, otherwise, replace values in dictionary with keys while leaving other features unaffected.

nomatch

an optional character naming a new key for tokens that do not matched to a dictionary values If NULL (default), do not record unmatched tokens.

append_key

if TRUE, annotate matched tokens with keys.

separator

a character to separate tokens and keys when append_key = TRUE.

concatenator

the concatenation character that will connect the words making up the multi-word sequences.

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", then tokens_lookup() will match the longer. When nested_scope = "key", this longer-match priority is applied only within the key, while "dictionary" applies it across keys, matching only the key with the longer pattern, not the matches nested within that longer pattern from other keys. See Details.

apply_if

logical vector of length ndoc(x); documents are modified only when corresponding values are TRUE, others are left unchanged.

verbose

print status messages if TRUE

Details

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.

Note: apply_if This applies the dictionary lookup only to documents that match the logical condition. When exclusive = TRUE (the default), however, this means that empty documents will be returned for those not meeting the condition, since no lookup will be applied and hence no tokens replaced by matching keys.

See Also

tokens_replace

Examples

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))
dfm(tokens_lookup(toks1, dict1, valuetype = "glob", verbose = TRUE, nomatch = "NONE"))

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"))

# 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_lookup(toks2, dict3, levels = 2)
tokens_lookup(toks2, dict3, levels = 1:2)
tokens_lookup(toks2, dict3, levels = 3)
tokens_lookup(toks2, dict3, levels = c(1,3))
tokens_lookup(toks2, dict3, levels = c(2,3))

# show unmatched tokens
tokens_lookup(toks2, dict3, nomatch = "_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_lookup(toks4, dict4, nested_scope = "dictionary", exclusive = FALSE)