README.md
In trinker/tagger: Part of Speech Tagging
tagger 
tagger wraps the NLP and openNLP packages for easier part of
speech tagging. tagger uses the openNLP annotator to compute
"Penn Treebank parse annotations using the Apache OpenNLP chunking
parser for English."
The main functions and descriptions are listed in the table below.
Function
Description
tag_pos
Tag parts of speech
select_tags
Select specific part of speech tags from tag_pos
count_tags
Cross tabs of tags by grouping variable
Table of Contents
Installation
To download the development version of tagger:
Download the zip
ball or tar
ball, decompress and
run R CMD INSTALL on it, or use the pacman package to install the
development version:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh(c(
"trinker/termco",
"trinker/coreNLPsetup",
"trinker/tagger"
))
Contact
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/trinker/tagger/issues
- send a pull request on: https://github.com/trinker/tagger/
- compose a friendly e-mail to: tyler.rinker@gmail.com
Examples
The following examples demonstrate some of the functionality of
tagger.
Load the Tools/Data
library(dplyr); library(tagger)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following object is masked from 'package:qdap':
##
## %>%
## The following object is masked from 'package:qdapTools':
##
## id
## The following objects are masked from 'package:qdapRegex':
##
## escape, explain
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data(presidential_debates_2012)
mwe <- data_frame(
person = c("Tyler", "Norah", "Tyler"),
talk = c(
"I need $54 to go to the movies.",
"They refuse to permit us to obtain the refuse permit",
"This is the tagger package; like it?"
)
)
Tagging
Let's begin with a minimal example.
tag_pos(mwe$talk)
## [1] "I/PRP need/VBP $/$ 54/CD to/TO go/VB to/TO the/DT movies/NNS ./."
## [2] "They/PRP refuse/VBP to/TO permit/VB us/PRP to/TO obtain/VB the/DT refuse/NN permit/NN"
## [3] "This/DT is/VBZ the/DT tagger/NN package/NN ;/: like/IN it/PRP ?/."
Note that the out put pretty pints but the underlying structure is
simply a lst of named vectors, where the elements in the vectors are the
tokens and the names are the part of speech tags. We can use c on the
object to see it's true structure.
tag_pos(mwe$talk) %>%
c()
## [[1]]
## PRP VBP $ CD TO VB TO DT
## "I" "need" "$" "54" "to" "go" "to" "the"
## NNS .
## "movies" "."
##
## [[2]]
## PRP VBP TO VB PRP TO VB DT
## "They" "refuse" "to" "permit" "us" "to" "obtain" "the"
## NN NN
## "refuse" "permit"
##
## [[3]]
## DT VBZ DT NN NN : IN
## "This" "is" "the" "tagger" "package" ";" "like"
## PRP .
## "it" "?"
Let's try it on a larger example, the built in
presidential_debates_2012 data set. It'll take 30 seconds or so to
run, depending on the machine.
tag_pos(presidential_debates_2012$dialogue)
## 1. We/PRP 'll/MD talk/VB about/IN specifically/RB ...
## 2. But/CC what/WP do/VBP you/PRP support/VB the/DT ...
## 3. What/WP I/PRP support/VBP is/VBZ no/DT change/NN ...
## 4. And/CC the/DT president/NN supports/VBZ taking/VBG ...
## 5. And/CC what/WP about/IN the/DT vouchers/NNS ?/.
## .
## .
## .
## 2908. Thank/VB you/PRP so/RB much/RB ./.
## 2909. Gentlemen/NNS ,/, thank/VB you/PRP both/DT so/RB ...
## 2910. That/DT brings/VBZ an/DT end/NN to/TO this/DT ...
## 2911. As/IN I/PRP always/RB do/VBP at/IN the/DT end/NN ...
## 2912. Good/JJ night/NN ./.
This output is built into tagger as the
presidential_debates_2012_pos data set, which we'll use form this
point on in the demo.
Note that the user may choose to use CoreNLP as a backend by setting
engine = "coreNLP". To ensure that coreNLP is setup properly use
check_setup.
Plotting
The user can generate a horizontal barplot of the used tags.
presidential_debates_2012_pos %>%
plot()
Interpreting Tags
The tags generated by openNLP are from Penn Treebank. As such there
are many tags, more than the few parts of speech we learned in grade
school. Remembering the meaning of each tags may be difficult, therefore
the penn_tags creates a left aligned data frame of the possible tags
and their meaning.
penn_tags()
## Tag Description
## 1 $ dollar
## 2 `` opening quotation mark
## 3 '' closing quotation mark
## 4 ( opening parenthesis
## 5 ) closing parenthesis
## 6 , comma
## 7 - dash
## 8 . sentence terminator
## 9 : colon or ellipsis
## 10 CC conjunction, coordinating
## 11 CD numeral, cardinal
## 12 DT determiner
## 13 EX existential there
## 14 FW foreign word
## 15 IN preposition or conjunction, subordinating
## 16 JJ adjective or numeral, ordinal
## 17 JJR adjective, comparative
## 18 JJS adjective, superlative
## 19 LS list item marker
## 20 MD modal auxiliary
## 21 NN noun, common, singular or mass
## 22 NNP noun, proper, singular
## 23 NNPS noun, proper, plural
## 24 NNS noun, common, plural
## 25 PDT pre-determiner
## 26 POS genitive marker
## 27 PRP pronoun, personal
## 28 PRP$ pronoun, possessive
## 29 RB adverb
## 30 RBR adverb, comparative
## 31 RBS adverb, superlative
## 32 RP particle
## 33 SYM symbol
## 34 TO "to" as preposition or infinitive marker
## 35 UH interjection
## 36 VB verb, base form
## 37 VBD verb, past tense
## 38 VBG verb, present participle or gerund
## 39 VBN verb, past participle
## 40 VBP verb, present tense, not 3rd person singular
## 41 VBZ verb, present tense, 3rd person singular
## 42 WDT WH-determiner
## 43 WP WH-pronoun
## 44 WP$ WH-pronoun, possessive
## 45 WRB Wh-adverb
Counts
The user can generate a count of the tags by grouping variable as well.
The number of columns explodes quickly, even with this minimal example.
tag_pos(mwe$talk) %>%
count_tags(mwe$person)
## person n.tokens $ . : CD DT IN
## 1 Norah 10 0 0 0 0 1(10.0%) 0
## 2 Tyler 19 1(5.3%) 2(10.5%) 1(5.3%) 1(5.3%) 3(15.8%) 1(5.3%)
## NN NNS PRP TO VB VBP VBZ
## 1 2(20.0%) 0 2(20.0%) 2(20.0%) 2(20.0%) 1(10.0%) 0
## 2 2(10.5%) 1(5.3%) 2(10.5%) 2(10.5%) 1(5.3%) 1(5.3%) 1(5.3%)
The default is a pretty printing (counts + proportions) that can be
turned off to print raw counts only.
tag_pos(mwe$talk) %>%
count_tags(mwe$person) %>%
print(pretty = FALSE)
## person n.tokens $ . : CD DT IN NN NNS PRP TO VB VBP VBZ
## 1: Tyler 19 1 2 1 1 3 1 2 1 2 2 1 1 1
## 2: Norah 10 0 0 0 0 1 0 2 0 2 2 2 1 0
Select Tags
The user may wish to select specific tags. The select_tags function
enables selection of specific tags via element matching (which can be
negated) or regular expression.
Here we select only the nouns.
presidential_debates_2012_pos %>%
select_tags(c("NN", "NNP", "NNPS", "NNS"))
## 1. health/NN care/NN moment/NN
## 2. voucher/NN system/NN Governor/NNP
## 3. change/NN retirees/NNS retirees/NNS Medicare/NNP
## 4. president/NN dollar/NN program/NN
## 5. vouchers/NNS
## .
## .
## .
## 2908.
## 2909. Gentlemen/NNS
## 2910. end/NN year/NN debates/NNS Lynn/NNP University/NNP ...
## 2911. end/NN debates/NNS words/NNS mom/NN vote/NN
## 2912. night/NN
This could also have been accomplished with a simpler regex call by
setting regex = TRUE.
presidential_debates_2012_pos %>%
select_tags("NN", regex=TRUE)
## 1. health/NN care/NN moment/NN
## 2. voucher/NN system/NN Governor/NNP
## 3. change/NN retirees/NNS retirees/NNS Medicare/NNP
## 4. president/NN dollar/NN program/NN
## 5. vouchers/NNS
## .
## .
## .
## 2908.
## 2909. Gentlemen/NNS
## 2910. end/NN year/NN debates/NNS Lynn/NNP University/NNP ...
## 2911. end/NN debates/NNS words/NNS mom/NN vote/NN
## 2912. night/NN
In this way we could quickly select the nouns and verbs with the
following call.
presidential_debates_2012_pos %>%
select_tags("^(VB|NN)", regex=TRUE)
## 1. talk/VB health/NN care/NN moment/NN
## 2. do/VBP support/VB voucher/NN system/NN Governor/NNP
## 3. support/VBP is/VBZ change/NN retirees/NNS ...
## 4. president/NN supports/VBZ taking/VBG dollar/NN ...
## 5. vouchers/NNS
## .
## .
## .
## 2908. Thank/VB
## 2909. Gentlemen/NNS thank/VB
## 2910. brings/VBZ end/NN year/NN debates/NNS want/VBP ...
## 2911. do/VBP end/NN debates/NNS leave/VBP words/NNS ...
## 2912. night/NN
Note that the output is a tag_pos class and the plotting,
count_tags, and as_word_tag functions can be used on the result.
presidential_debates_2012_pos %>%
select_tags("^(VB|NN)", regex=TRUE) %>%
plot()
presidential_debates_2012_pos %>%
select_tags("^(VB|NN)", regex=TRUE) %>%
count_tags()
## # A tibble: 2,912 × 11
## n.tokens NN NNP NNPS NNS VB VBD VBG
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 4 3(75.0%) 0 0 0 1(25.0%) 0 0
## 2 5 2(40.0%) 1(20.0%) 0 0 1(20.0%) 0 0
## 3 6 1(16.7%) 1(16.7%) 0 2(33.3%) 0 0 0
## 4 5 3(60.0%) 0 0 0 0 0 1(20.0%)
## 5 1 0 0 0 1(100.0%) 0 0 0
## 6 3 1(33.3%) 0 0 0 0 0 0
## 7 16 4(25.0%) 2(12.5%) 0 1(6.2%) 4(25.0%) 0 1(6.2%)
## 8 1 1(100.0%) 0 0 0 0 0 0
## 9 6 1(16.7%) 0 0 1(16.7%) 3(50.0%) 0 0
## 10 5 2(40.0%) 0 0 0 2(40.0%) 0 0
## # ... with 2,902 more rows, and 3 more variables: VBN <chr>, VBP <chr>,
## # VBZ <chr>
Altering Tag Display
As Word Tags
The traditional way to display tags is to incorporate them into the
sentence, placing them after/before their respective token, separated by
a forward slash (e.g., talk/VB). This is the default printing style of
tag_pos though not truly the structure of the output. The user can
coerce the underlying structure with the as_word_tag function,
converting the named list of vectors into a list of part of speech
incorporated, unnamed vectors. Below I only print the first 6 elements
of as_word_tag.
presidential_debates_2012_pos %>%
as_word_tag() %>%
head()
## [1] "We/PRP 'll/MD talk/VB about/IN specifically/RB about/IN health/NN care/NN in/IN a/DT moment/NN ./."
## [2] "But/CC what/WP do/VBP you/PRP support/VB the/DT voucher/NN system/NN ,/, Governor/NNP ?/."
## [3] "What/WP I/PRP support/VBP is/VBZ no/DT change/NN for/IN current/JJ retirees/NNS and/CC near/IN retirees/NNS to/TO Medicare/NNP ./."
## [4] "And/CC the/DT president/NN supports/VBZ taking/VBG dollar/NN seven/CD hundred/CD sixteen/CD billion/CD out/IN of/IN that/DT program/NN ./."
## [5] "And/CC what/WP about/IN the/DT vouchers/NNS ?/."
## [6] "So/IN that/DT 's/VBZ that/DT 's/VBZ number/NN one/CD ./."
As Tuples
Python uses a tuple construction of parts of speech to display tags.
This can be a useful structure. Essentially the structure is a lists of
lists of two element vectors. Each vector contains a word and a part of
speech tag. as_tuple uses the following R structuring:
list(list(c("word", "tag"), c("word", "tag")), list(c("word", "tag")))
but prints to the console in the Python way. Using
print(as_tuple(x), truncate=Inf, file="out.txt") allows the user to
print to an external file.
tag_pos(mwe$talk) %>%
as_tuple() %>%
print(truncate=Inf)
## [[("I", "PRP"), ("need", "VBP"), ("$", "$"), ("54", "CD"), ("to", "TO"), ("go", "VB"), ("to", "TO"), ("the", "DT"), ("movies", "NNS"), (".", ".")], [("They", "PRP"), ("refuse", "VBP"), ("to", "TO"), ("permit", "VB"), ("us", "PRP"), ("to", "TO"), ("obtain", "VB"), ("the", "DT"), ("refuse", "NN"), ("permit", "NN")], [("This", "DT"), ("is", "VBZ"), ("the", "DT"), ("tagger", "NN"), ("package", "NN"), (";", ":"), ("like", "IN"), ("it", "PRP"), ("?", ".")]]
As Universal Tags
Petrov, Das, & McDonald
(2011) provide a
mapping to convert Penn Treebank tags into universal part of speech
tags. The as_universal function harnesses this mapping.
tag_pos(mwe$talk) %>%
as_universal()
## [1] "I/PRON need/VERB $/. 54/NUM to/PRT go/VERB to/PRT the/DET movies/NOUN ./."
## [2] "They/PRON refuse/VERB to/PRT permit/VERB us/PRON to/PRT obtain/VERB the/DET refuse/NOUN permit/NOUN"
## [3] "This/DET is/VERB the/DET tagger/NOUN package/NOUN ;/. like/ADP it/PRON ?/."
The out put is a tag_pos object and thus has a generic plot method.
tag_pos(mwe$talk) %>%
as_universal() %>%
plot()
tag_pos(mwe$talk) %>%
as_universal() %>%
count_tags()
## n.tokens . ADP DET NOUN NUM PRON PRT
## 1 10 2(20.0%) 0 1(10.0%) 1(10.0%) 1(10.0%) 1(10.0%) 2(20.0%)
## 2 10 0 0 1(10.0%) 2(20.0%) 0 2(20.0%) 2(20.0%)
## 3 9 2(22.2%) 1(11.1%) 2(22.2%) 2(22.2%) 0 1(11.1%) 0
## VERB
## 1 2(20.0%)
## 2 3(30.0%)
## 3 1(11.1%)
As Basic Tags
as_basic provides an even more coarse tagset than as_universal.
Basic tags include: (a) nouns, (b) adjectives, (c) prepositions,
(d) articles, (e) verb, (f) pronouns, (g) adverbs, (h)
interjections, & (i) conjunctions. The X and . tags are retained
for punctuation and unclassified parts of speech.
tag_pos(mwe$talk) %>%
as_basic()
## [1] "I/pronoun need/verb $/. 54/adjective to/preposition go/verb to/preposition the/article movies/noun ./."
## [2] "They/pronoun refuse/verb to/preposition permit/verb us/pronoun to/preposition obtain/verb the/article refuse/noun permit/noun"
## [3] "This/adjective is/verb the/article tagger/noun package/noun ;/. like/preposition it/pronoun ?/."
This tagset can be useful for more coarse purposes, including formality
(Heylighen & Dewaele, 2002) scoring.
- Heylighen, F., & Dewaele, J.M. (2002). Variation in the
contextuality of language: An empirical measure. Context in Context,
Special issue of Foundations of Science, 7 (3), 293-340.
The output is a tag_pos object and thus has a generic plot method.
tag_pos(mwe$talk) %>%
as_basic() %>%
plot()
tag_pos(mwe$talk) %>%
as_basic() %>%
count_tags()
## n.tokens . adjective article noun preposition pronoun
## 1 10 2(20.0%) 1(10.0%) 1(10.0%) 1(10.0%) 2(20.0%) 1(10.0%)
## 2 10 0 0 1(10.0%) 2(20.0%) 2(20.0%) 2(20.0%)
## 3 9 2(22.2%) 1(11.1%) 1(11.1%) 2(22.2%) 1(11.1%) 1(11.1%)
## verb
## 1 2(20.0%)
## 2 3(30.0%)
## 3 1(11.1%)
trinker/tagger documentation built on May 31, 2019, 10:42 p.m.
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tagger
tagger wraps the NLP and openNLP packages for easier part of speech tagging. tagger uses the openNLP annotator to compute "Penn Treebank parse annotations using the Apache OpenNLP chunking parser for English."
The main functions and descriptions are listed in the table below.
Function Descriptiontag_pos
Tag parts of speech
select_tags
Select specific part of speech tags from tag_pos
count_tags
Cross tabs of tags by grouping variable
Table of Contents
Installation
To download the development version of tagger:
Download the zip
ball or tar
ball, decompress and
run R CMD INSTALL on it, or use the pacman package to install the
development version:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh(c(
"trinker/termco",
"trinker/coreNLPsetup",
"trinker/tagger"
))
Contact
You are welcome to: - submit suggestions and bug-reports at: https://github.com/trinker/tagger/issues - send a pull request on: https://github.com/trinker/tagger/ - compose a friendly e-mail to: tyler.rinker@gmail.com
Examples
The following examples demonstrate some of the functionality of tagger.
Load the Tools/Data
library(dplyr); library(tagger)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following object is masked from 'package:qdap':
##
## %>%
## The following object is masked from 'package:qdapTools':
##
## id
## The following objects are masked from 'package:qdapRegex':
##
## escape, explain
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data(presidential_debates_2012)
mwe <- data_frame(
person = c("Tyler", "Norah", "Tyler"),
talk = c(
"I need $54 to go to the movies.",
"They refuse to permit us to obtain the refuse permit",
"This is the tagger package; like it?"
)
)
Tagging
Let's begin with a minimal example.
tag_pos(mwe$talk)
## [1] "I/PRP need/VBP $/$ 54/CD to/TO go/VB to/TO the/DT movies/NNS ./."
## [2] "They/PRP refuse/VBP to/TO permit/VB us/PRP to/TO obtain/VB the/DT refuse/NN permit/NN"
## [3] "This/DT is/VBZ the/DT tagger/NN package/NN ;/: like/IN it/PRP ?/."
Note that the out put pretty pints but the underlying structure is
simply a lst of named vectors, where the elements in the vectors are the
tokens and the names are the part of speech tags. We can use c on the
object to see it's true structure.
tag_pos(mwe$talk) %>%
c()
## [[1]]
## PRP VBP $ CD TO VB TO DT
## "I" "need" "$" "54" "to" "go" "to" "the"
## NNS .
## "movies" "."
##
## [[2]]
## PRP VBP TO VB PRP TO VB DT
## "They" "refuse" "to" "permit" "us" "to" "obtain" "the"
## NN NN
## "refuse" "permit"
##
## [[3]]
## DT VBZ DT NN NN : IN
## "This" "is" "the" "tagger" "package" ";" "like"
## PRP .
## "it" "?"
Let's try it on a larger example, the built in
presidential_debates_2012 data set. It'll take 30 seconds or so to
run, depending on the machine.
tag_pos(presidential_debates_2012$dialogue)
## 1. We/PRP 'll/MD talk/VB about/IN specifically/RB ...
## 2. But/CC what/WP do/VBP you/PRP support/VB the/DT ...
## 3. What/WP I/PRP support/VBP is/VBZ no/DT change/NN ...
## 4. And/CC the/DT president/NN supports/VBZ taking/VBG ...
## 5. And/CC what/WP about/IN the/DT vouchers/NNS ?/.
## .
## .
## .
## 2908. Thank/VB you/PRP so/RB much/RB ./.
## 2909. Gentlemen/NNS ,/, thank/VB you/PRP both/DT so/RB ...
## 2910. That/DT brings/VBZ an/DT end/NN to/TO this/DT ...
## 2911. As/IN I/PRP always/RB do/VBP at/IN the/DT end/NN ...
## 2912. Good/JJ night/NN ./.
This output is built into tagger as the
presidential_debates_2012_pos data set, which we'll use form this
point on in the demo.
Note that the user may choose to use CoreNLP as a backend by setting
engine = "coreNLP". To ensure that coreNLP is setup properly use
check_setup.
Plotting
The user can generate a horizontal barplot of the used tags.
presidential_debates_2012_pos %>%
plot()
Interpreting Tags
The tags generated by openNLP are from Penn Treebank. As such there
are many tags, more than the few parts of speech we learned in grade
school. Remembering the meaning of each tags may be difficult, therefore
the penn_tags creates a left aligned data frame of the possible tags
and their meaning.
penn_tags()
## Tag Description
## 1 $ dollar
## 2 `` opening quotation mark
## 3 '' closing quotation mark
## 4 ( opening parenthesis
## 5 ) closing parenthesis
## 6 , comma
## 7 - dash
## 8 . sentence terminator
## 9 : colon or ellipsis
## 10 CC conjunction, coordinating
## 11 CD numeral, cardinal
## 12 DT determiner
## 13 EX existential there
## 14 FW foreign word
## 15 IN preposition or conjunction, subordinating
## 16 JJ adjective or numeral, ordinal
## 17 JJR adjective, comparative
## 18 JJS adjective, superlative
## 19 LS list item marker
## 20 MD modal auxiliary
## 21 NN noun, common, singular or mass
## 22 NNP noun, proper, singular
## 23 NNPS noun, proper, plural
## 24 NNS noun, common, plural
## 25 PDT pre-determiner
## 26 POS genitive marker
## 27 PRP pronoun, personal
## 28 PRP$ pronoun, possessive
## 29 RB adverb
## 30 RBR adverb, comparative
## 31 RBS adverb, superlative
## 32 RP particle
## 33 SYM symbol
## 34 TO "to" as preposition or infinitive marker
## 35 UH interjection
## 36 VB verb, base form
## 37 VBD verb, past tense
## 38 VBG verb, present participle or gerund
## 39 VBN verb, past participle
## 40 VBP verb, present tense, not 3rd person singular
## 41 VBZ verb, present tense, 3rd person singular
## 42 WDT WH-determiner
## 43 WP WH-pronoun
## 44 WP$ WH-pronoun, possessive
## 45 WRB Wh-adverb
Counts
The user can generate a count of the tags by grouping variable as well. The number of columns explodes quickly, even with this minimal example.
tag_pos(mwe$talk) %>%
count_tags(mwe$person)
## person n.tokens $ . : CD DT IN
## 1 Norah 10 0 0 0 0 1(10.0%) 0
## 2 Tyler 19 1(5.3%) 2(10.5%) 1(5.3%) 1(5.3%) 3(15.8%) 1(5.3%)
## NN NNS PRP TO VB VBP VBZ
## 1 2(20.0%) 0 2(20.0%) 2(20.0%) 2(20.0%) 1(10.0%) 0
## 2 2(10.5%) 1(5.3%) 2(10.5%) 2(10.5%) 1(5.3%) 1(5.3%) 1(5.3%)
The default is a pretty printing (counts + proportions) that can be turned off to print raw counts only.
tag_pos(mwe$talk) %>%
count_tags(mwe$person) %>%
print(pretty = FALSE)
## person n.tokens $ . : CD DT IN NN NNS PRP TO VB VBP VBZ
## 1: Tyler 19 1 2 1 1 3 1 2 1 2 2 1 1 1
## 2: Norah 10 0 0 0 0 1 0 2 0 2 2 2 1 0
Select Tags
The user may wish to select specific tags. The select_tags function
enables selection of specific tags via element matching (which can be
negated) or regular expression.
Here we select only the nouns.
presidential_debates_2012_pos %>%
select_tags(c("NN", "NNP", "NNPS", "NNS"))
## 1. health/NN care/NN moment/NN
## 2. voucher/NN system/NN Governor/NNP
## 3. change/NN retirees/NNS retirees/NNS Medicare/NNP
## 4. president/NN dollar/NN program/NN
## 5. vouchers/NNS
## .
## .
## .
## 2908.
## 2909. Gentlemen/NNS
## 2910. end/NN year/NN debates/NNS Lynn/NNP University/NNP ...
## 2911. end/NN debates/NNS words/NNS mom/NN vote/NN
## 2912. night/NN
This could also have been accomplished with a simpler regex call by
setting regex = TRUE.
presidential_debates_2012_pos %>%
select_tags("NN", regex=TRUE)
## 1. health/NN care/NN moment/NN
## 2. voucher/NN system/NN Governor/NNP
## 3. change/NN retirees/NNS retirees/NNS Medicare/NNP
## 4. president/NN dollar/NN program/NN
## 5. vouchers/NNS
## .
## .
## .
## 2908.
## 2909. Gentlemen/NNS
## 2910. end/NN year/NN debates/NNS Lynn/NNP University/NNP ...
## 2911. end/NN debates/NNS words/NNS mom/NN vote/NN
## 2912. night/NN
In this way we could quickly select the nouns and verbs with the following call.
presidential_debates_2012_pos %>%
select_tags("^(VB|NN)", regex=TRUE)
## 1. talk/VB health/NN care/NN moment/NN
## 2. do/VBP support/VB voucher/NN system/NN Governor/NNP
## 3. support/VBP is/VBZ change/NN retirees/NNS ...
## 4. president/NN supports/VBZ taking/VBG dollar/NN ...
## 5. vouchers/NNS
## .
## .
## .
## 2908. Thank/VB
## 2909. Gentlemen/NNS thank/VB
## 2910. brings/VBZ end/NN year/NN debates/NNS want/VBP ...
## 2911. do/VBP end/NN debates/NNS leave/VBP words/NNS ...
## 2912. night/NN
Note that the output is a tag_pos class and the plotting,
count_tags, and as_word_tag functions can be used on the result.
presidential_debates_2012_pos %>%
select_tags("^(VB|NN)", regex=TRUE) %>%
plot()
presidential_debates_2012_pos %>%
select_tags("^(VB|NN)", regex=TRUE) %>%
count_tags()
## # A tibble: 2,912 × 11
## n.tokens NN NNP NNPS NNS VB VBD VBG
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 4 3(75.0%) 0 0 0 1(25.0%) 0 0
## 2 5 2(40.0%) 1(20.0%) 0 0 1(20.0%) 0 0
## 3 6 1(16.7%) 1(16.7%) 0 2(33.3%) 0 0 0
## 4 5 3(60.0%) 0 0 0 0 0 1(20.0%)
## 5 1 0 0 0 1(100.0%) 0 0 0
## 6 3 1(33.3%) 0 0 0 0 0 0
## 7 16 4(25.0%) 2(12.5%) 0 1(6.2%) 4(25.0%) 0 1(6.2%)
## 8 1 1(100.0%) 0 0 0 0 0 0
## 9 6 1(16.7%) 0 0 1(16.7%) 3(50.0%) 0 0
## 10 5 2(40.0%) 0 0 0 2(40.0%) 0 0
## # ... with 2,902 more rows, and 3 more variables: VBN <chr>, VBP <chr>,
## # VBZ <chr>
Altering Tag Display
As Word Tags
The traditional way to display tags is to incorporate them into the
sentence, placing them after/before their respective token, separated by
a forward slash (e.g., talk/VB). This is the default printing style of
tag_pos though not truly the structure of the output. The user can
coerce the underlying structure with the as_word_tag function,
converting the named list of vectors into a list of part of speech
incorporated, unnamed vectors. Below I only print the first 6 elements
of as_word_tag.
presidential_debates_2012_pos %>%
as_word_tag() %>%
head()
## [1] "We/PRP 'll/MD talk/VB about/IN specifically/RB about/IN health/NN care/NN in/IN a/DT moment/NN ./."
## [2] "But/CC what/WP do/VBP you/PRP support/VB the/DT voucher/NN system/NN ,/, Governor/NNP ?/."
## [3] "What/WP I/PRP support/VBP is/VBZ no/DT change/NN for/IN current/JJ retirees/NNS and/CC near/IN retirees/NNS to/TO Medicare/NNP ./."
## [4] "And/CC the/DT president/NN supports/VBZ taking/VBG dollar/NN seven/CD hundred/CD sixteen/CD billion/CD out/IN of/IN that/DT program/NN ./."
## [5] "And/CC what/WP about/IN the/DT vouchers/NNS ?/."
## [6] "So/IN that/DT 's/VBZ that/DT 's/VBZ number/NN one/CD ./."
As Tuples
Python uses a tuple construction of parts of speech to display tags.
This can be a useful structure. Essentially the structure is a lists of
lists of two element vectors. Each vector contains a word and a part of
speech tag. as_tuple uses the following R structuring:
list(list(c("word", "tag"), c("word", "tag")), list(c("word", "tag")))
but prints to the console in the Python way. Using
print(as_tuple(x), truncate=Inf, file="out.txt") allows the user to
print to an external file.
tag_pos(mwe$talk) %>%
as_tuple() %>%
print(truncate=Inf)
## [[("I", "PRP"), ("need", "VBP"), ("$", "$"), ("54", "CD"), ("to", "TO"), ("go", "VB"), ("to", "TO"), ("the", "DT"), ("movies", "NNS"), (".", ".")], [("They", "PRP"), ("refuse", "VBP"), ("to", "TO"), ("permit", "VB"), ("us", "PRP"), ("to", "TO"), ("obtain", "VB"), ("the", "DT"), ("refuse", "NN"), ("permit", "NN")], [("This", "DT"), ("is", "VBZ"), ("the", "DT"), ("tagger", "NN"), ("package", "NN"), (";", ":"), ("like", "IN"), ("it", "PRP"), ("?", ".")]]
As Universal Tags
Petrov, Das, & McDonald
(2011) provide a
mapping to convert Penn Treebank tags into universal part of speech
tags. The as_universal function harnesses this mapping.
tag_pos(mwe$talk) %>%
as_universal()
## [1] "I/PRON need/VERB $/. 54/NUM to/PRT go/VERB to/PRT the/DET movies/NOUN ./."
## [2] "They/PRON refuse/VERB to/PRT permit/VERB us/PRON to/PRT obtain/VERB the/DET refuse/NOUN permit/NOUN"
## [3] "This/DET is/VERB the/DET tagger/NOUN package/NOUN ;/. like/ADP it/PRON ?/."
The out put is a tag_pos object and thus has a generic plot method.
tag_pos(mwe$talk) %>%
as_universal() %>%
plot()
tag_pos(mwe$talk) %>%
as_universal() %>%
count_tags()
## n.tokens . ADP DET NOUN NUM PRON PRT
## 1 10 2(20.0%) 0 1(10.0%) 1(10.0%) 1(10.0%) 1(10.0%) 2(20.0%)
## 2 10 0 0 1(10.0%) 2(20.0%) 0 2(20.0%) 2(20.0%)
## 3 9 2(22.2%) 1(11.1%) 2(22.2%) 2(22.2%) 0 1(11.1%) 0
## VERB
## 1 2(20.0%)
## 2 3(30.0%)
## 3 1(11.1%)
As Basic Tags
as_basic provides an even more coarse tagset than as_universal.
Basic tags include: (a) nouns, (b) adjectives, (c) prepositions,
(d) articles, (e) verb, (f) pronouns, (g) adverbs, (h)
interjections, & (i) conjunctions. The X and . tags are retained
for punctuation and unclassified parts of speech.
tag_pos(mwe$talk) %>%
as_basic()
## [1] "I/pronoun need/verb $/. 54/adjective to/preposition go/verb to/preposition the/article movies/noun ./."
## [2] "They/pronoun refuse/verb to/preposition permit/verb us/pronoun to/preposition obtain/verb the/article refuse/noun permit/noun"
## [3] "This/adjective is/verb the/article tagger/noun package/noun ;/. like/preposition it/pronoun ?/."
This tagset can be useful for more coarse purposes, including formality (Heylighen & Dewaele, 2002) scoring.
- Heylighen, F., & Dewaele, J.M. (2002). Variation in the contextuality of language: An empirical measure. Context in Context, Special issue of Foundations of Science, 7 (3), 293-340.
The output is a tag_pos object and thus has a generic plot method.
tag_pos(mwe$talk) %>%
as_basic() %>%
plot()
tag_pos(mwe$talk) %>%
as_basic() %>%
count_tags()
## n.tokens . adjective article noun preposition pronoun
## 1 10 2(20.0%) 1(10.0%) 1(10.0%) 1(10.0%) 2(20.0%) 1(10.0%)
## 2 10 0 0 1(10.0%) 2(20.0%) 2(20.0%) 2(20.0%)
## 3 9 2(22.2%) 1(11.1%) 1(11.1%) 2(22.2%) 1(11.1%) 1(11.1%)
## verb
## 1 2(20.0%)
## 2 3(30.0%)
## 3 1(11.1%)
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