spacyr provides a convenient R wrapper around the Python spaCy package. It offers easy access to the following functionality of spaCy:
It also allows a user to request additional token-level attributes directly from spaCy.
spacyr also takes care of the installation of not only spaCy but also Python itself, in a self-contained miniconda or virtualenv environment, and can install additional language models or upgrade spaCy as new models and versions become available.
Finally, spacyr works seamlessly with the quanteda package, although such use is optional.
spacyr works through the reticulate package that allows R to harness the power of Python. To access the underlying Python functionality, spacyr must open a connection by being initialized within your R session.
We provide a function for this, spacy_initialize(),
which attempts to make this process as painless as possible. When spaCy
has been installed in a conda environment with
spacy_install() (and see https://spacyr.quanteda.io for detailed instructions on
this setup), spacy_initialize() automatically detects it
and initializes spaCy. If spaCy is installed in a normal environment
(i.e. not in a condaenv or virtualenv), spacy_initialize()
searches your system for Python executables, and testing which have
spaCy installed.
For power users with a specialized setup of spaCy (i.e. users who have a conda environment already set up for spaCy), it is possible to specify which environment or python executable to be used through one of the following methods:
condaenv argument: supplying the name of conda
environmentvirtualenv argument: supplying the path to the python
virtual environmentpython_executable argument: supplying the path to the
pythonThe spacy_parse() function is spacyr’s
main workhorse. It calls spaCy both to tokenize and tag the texts. It
provides two options for part of speech tagging, plus options to return
word lemmas, recognize names entities or noun phrases recognition, and
identify grammatical structures features by parsing syntactic
dependencies. It returns a data.frame corresponding to the
emerging text
interchange format for token data.frames.
The tokenization approach taken by spaCy is inclusive: it includes all tokens without restrictions, including punctuation characters and symbols.
Example:
txt <- c(d1 = "spaCy is great at fast natural language processing.",
d2 = "Mr. Smith spent two years in North Carolina.")
# process documents and obtain a data.table
parsedtxt <- spacy_parse(txt)
parsedtxt
## doc_id sentence_id token_id token lemma pos entity
## 1 d1 1 1 spaCy spacy INTJ
## 2 d1 1 2 is be AUX
## 3 d1 1 3 great great ADJ
## 4 d1 1 4 at at ADP
## 5 d1 1 5 fast fast ADJ
## 6 d1 1 6 natural natural ADJ
## 7 d1 1 7 language language NOUN
## 8 d1 1 8 processing processing NOUN
## 9 d1 1 9 . . PUNCT
## 10 d2 1 1 Mr. Mr. PROPN
## 11 d2 1 2 Smith Smith PROPN PERSON_B
## 12 d2 1 3 spent spend VERB
## 13 d2 1 4 two two NUM DATE_B
## 14 d2 1 5 years year NOUN DATE_I
## 15 d2 1 6 in in ADP
## 16 d2 1 7 North North PROPN GPE_B
## 17 d2 1 8 Carolina Carolina PROPN GPE_I
## 18 d2 1 9 . . PUNCTTwo fields are available for part-of-speech tags. The
pos field returned is the Universal tagset
for parts-of-speech, a general scheme that most users will find
serves their needs, and also that provides equivalencies across
languages. spacyr also provides a more detailed tagset,
defined in each spaCy language model. For English, this is the OntoNotes
5 version of the Penn Treebank tag set.
spacy_parse(txt, tag = TRUE, entity = FALSE, lemma = FALSE)
## doc_id sentence_id token_id token pos tag
## 1 d1 1 1 spaCy INTJ UH
## 2 d1 1 2 is AUX VBZ
## 3 d1 1 3 great ADJ JJ
## 4 d1 1 4 at ADP IN
## 5 d1 1 5 fast ADJ JJ
## 6 d1 1 6 natural ADJ JJ
## 7 d1 1 7 language NOUN NN
## 8 d1 1 8 processing NOUN NN
## 9 d1 1 9 . PUNCT .
## 10 d2 1 1 Mr. PROPN NNP
## 11 d2 1 2 Smith PROPN NNP
## 12 d2 1 3 spent VERB VBD
## 13 d2 1 4 two NUM CD
## 14 d2 1 5 years NOUN NNS
## 15 d2 1 6 in ADP IN
## 16 d2 1 7 North PROPN NNP
## 17 d2 1 8 Carolina PROPN NNP
## 18 d2 1 9 . PUNCT .The Penn Treebank is specific to English parts of speech. For other
language models, the detailed tagset will be based on a different
scheme. In the German language model, for instance, the universal tagset
(pos) remains the same, but the detailed tagset
(tag) is based on the TIGER
Treebank scheme. Full details are available from the spaCy models web page.
Direct parsing of texts is also possible, using
spacy_tokenize(). The options are designed to match
those in the tokens()
function from the quanteda package. By default this
returns a named list (where the document name is the list element
name):
spacy_tokenize(txt)
## $d1
## [1] "spaCy" "is" "great" "at" "fast"
## [6] "natural" "language" "processing" "."
##
## $d2
## [1] "Mr." "Smith" "spent" "two" "years" "in" "North"
## [8] "Carolina" "."but it can also output a data.frame:
spacyr can extract entities, either named or “extended”
from the output of spacy_parse().
parsedtxt <- spacy_parse(txt, lemma = FALSE, entity = TRUE, nounphrase = TRUE)
entity_extract(parsedtxt)
## doc_id sentence_id entity entity_type
## 1 d2 1 Smith PERSON
## 2 d2 1 North_Carolina GPE“Extended” entities including entities such as dates, events, and cardinal or ordinal quantities.
entity_extract(parsedtxt, type = "all")
## doc_id sentence_id entity entity_type
## 1 d2 1 Smith PERSON
## 2 d2 1 two_years DATE
## 3 d2 1 North_Carolina GPEOne very useful feature is to use the consolidation functions to compound multi-word entities into single “tokens” (as they would in a language like German):
entity_consolidate(parsedtxt) %>%
tail()
## doc_id sentence_id token_id token pos entity_type
## 11 d2 1 2 Smith ENTITY PERSON
## 12 d2 1 3 spent VERB
## 13 d2 1 4 two_years ENTITY DATE
## 14 d2 1 5 in ADP
## 15 d2 1 6 North_Carolina ENTITY GPE
## 16 d2 1 7 . PUNCTIn a similar manner to named entity extraction, spacyr can extract or concatenate [noun phrases* (or noun chunks).
nounphrase_extract(parsedtxt)
## doc_id sentence_id nounphrase
## 1 d1 1 fast_natural_language_processing
## 2 d2 1 Mr._Smith
## 3 d2 1 two_years
## 4 d2 1 North_CarolinaJust as with entities, noun phrases can also be consolidated into single “tokens”:
nounphrase_consolidate(parsedtxt)
## doc_id sentence_id token_id token pos
## 1 d1 1 1 spaCy INTJ
## 2 d1 1 2 is AUX
## 3 d1 1 3 great ADJ
## 4 d1 1 4 at ADP
## 5 d1 1 5 fast_natural_language_processing nounphrase
## 6 d1 1 6 . PUNCT
## 7 d2 1 1 Mr._Smith nounphrase
## 8 d2 1 2 spent VERB
## 9 d2 1 3 two_years nounphrase
## 10 d2 1 4 in ADP
## 11 d2 1 5 North_Carolina nounphrase
## 12 d2 1 6 . PUNCTIf a user’s only goal is entity or noun phrase extraction, then two functions make this easy without first parsing the entire text:
spacy_extract_entity(txt)
## doc_id text ent_type start_id length
## 1 d2 Smith PERSON 2 1
## 2 d2 two years DATE 4 2
## 3 d2 North Carolina GPE 7 2
spacy_extract_nounphrases(txt)
## doc_id text root_text start_id root_id length
## 1 d1 fast natural language processing processing 5 8 4
## 2 d2 Mr. Smith Smith 1 2 2
## 3 d2 two years years 4 5 2
## 4 d2 North Carolina Carolina 7 8 2Detailed parsing of syntactic dependencies is possible with the
dependency = TRUE option:
spacy_parse(txt, dependency = TRUE, lemma = FALSE, pos = FALSE)
## doc_id sentence_id token_id token head_token_id dep_rel entity
## 1 d1 1 1 spaCy 2 nsubj
## 2 d1 1 2 is 2 ROOT
## 3 d1 1 3 great 2 acomp
## 4 d1 1 4 at 2 prep
## 5 d1 1 5 fast 8 amod
## 6 d1 1 6 natural 7 amod
## 7 d1 1 7 language 8 compound
## 8 d1 1 8 processing 4 pobj
## 9 d1 1 9 . 2 punct
## 10 d2 1 1 Mr. 2 compound
## 11 d2 1 2 Smith 3 nsubj PERSON_B
## 12 d2 1 3 spent 3 ROOT
## 13 d2 1 4 two 5 nummod DATE_B
## 14 d2 1 5 years 3 dobj DATE_I
## 15 d2 1 6 in 3 prep
## 16 d2 1 7 North 8 compound GPE_B
## 17 d2 1 8 Carolina 6 pobj GPE_I
## 18 d2 1 9 . 3 punctIt is also possible to extract additional attributes of spaCy
tokens with the additional_attributes option. For
example, detecting numbers and email addresses:
spacy_parse("I have six email addresses, including me@mymail.com.",
additional_attributes = c("like_num", "like_email"),
lemma = FALSE, pos = FALSE, entity = FALSE)
## doc_id sentence_id token_id token like_num like_email
## 1 text1 1 1 I FALSE FALSE
## 2 text1 1 2 have FALSE FALSE
## 3 text1 1 3 six TRUE FALSE
## 4 text1 1 4 email FALSE FALSE
## 5 text1 1 5 addresses FALSE FALSE
## 6 text1 1 6 , FALSE FALSE
## 7 text1 1 7 including FALSE FALSE
## 8 text1 1 8 me@mymail.com FALSE TRUE
## 9 text1 1 9 . FALSE FALSEBy default, spacyr loads an English language model.
You also can load spaCy’s other language models or use one
of the language
models with alpha support by specifying the model
option when calling spacy_initialize(). We have
successfully tested following language models with spaCy version
2.0.18.
| Language | ModelName |
|---|---|
| German | de |
| Spanish | es |
| Portuguese | pt |
| French | fr |
| Italian | it |
| Dutch | nl |
This is an example of parsing German texts.
## first finalize the old instance of spaCy if it's loaded
spacy_finalize()
spacy_initialize(model = "de_core_news_sm")
## successfully initialized (spaCy Version: 3.7.2, language model: de_core_news_sm)
txt_german <- c(R = "R ist eine freie Programmiersprache für statistische Berechnungen und Grafiken. Sie wurde von Statistikern für Anwender mit statistischen Aufgaben entwickelt.",
python = "Python ist eine universelle, üblicherweise interpretierte höhere Programmiersprache. Sie will einen gut lesbaren, knappen Programmierstil fördern.")
results_german <- spacy_parse(txt_german, dependency = FALSE, lemma = FALSE, tag = TRUE)
results_german
## doc_id sentence_id token_id token pos tag entity
## 1 R 1 1 R X XY
## 2 R 1 2 ist AUX VAFIN
## 3 R 1 3 eine DET ART
## 4 R 1 4 freie ADJ ADJA
## 5 R 1 5 Programmiersprache NOUN NN
## 6 R 1 6 für ADP APPR
## 7 R 1 7 statistische ADJ ADJA
## 8 R 1 8 Berechnungen NOUN NN
## 9 R 1 9 und CCONJ KON
## 10 R 1 10 Grafiken NOUN NN
## 11 R 1 11 . PUNCT $.
## 12 R 2 1 Sie PRON PPER
## 13 R 2 2 wurde AUX VAFIN
## 14 R 2 3 von ADP APPR
## 15 R 2 4 Statistikern NOUN NN
## 16 R 2 5 für ADP APPR
## 17 R 2 6 Anwender NOUN NN
## 18 R 2 7 mit ADP APPR
## 19 R 2 8 statistischen ADJ ADJA
## 20 R 2 9 Aufgaben NOUN NN
## 21 R 2 10 entwickelt VERB VVPP
## 22 R 2 11 . PUNCT $.
## 23 python 1 1 Python NOUN NN MISC_B
## 24 python 1 2 ist AUX VAFIN
## 25 python 1 3 eine DET ART
## 26 python 1 4 universelle ADJ ADJA
## 27 python 1 5 , PUNCT $,
## 28 python 1 6 üblicherweise ADV ADV
## 29 python 1 7 interpretierte ADJ ADJA
## 30 python 1 8 höhere ADJ ADJA
## 31 python 1 9 Programmiersprache NOUN NN
## 32 python 1 10 . PUNCT $.
## 33 python 2 1 Sie PRON PPER
## 34 python 2 2 will AUX VMFIN
## 35 python 2 3 einen DET ART
## 36 python 2 4 gut ADV ADJD
## 37 python 2 5 lesbaren ADJ ADJA
## 38 python 2 6 , PUNCT $,
## 39 python 2 7 knappen ADJ ADJA
## 40 python 2 8 Programmierstil NOUN NN
## 41 python 2 9 fördern VERB VVINF
## 42 python 2 10 . PUNCT $.
spacy_finalize()Note that the additional language models must first be installed in
spaCy. When spaCy has been installed through
spacy_install(), installation of additional language models
is very simple. For example, the German language model can be installed
(spacy_download_langmodel("de_core_news_sm")). In other
environments, you can install the model by entering
python -m spacy download de in the console.
The outputs and formats of spacyr are designed to integrate directly with the quanteda package.
For instance, many of its functions operate directly on spacyr objects, such as a parsed text.
require(quanteda, warn.conflicts = FALSE, quietly = TRUE)
docnames(parsedtxt)
## [1] "d1" "d2"
ndoc(parsedtxt)
## [1] 2
ntoken(parsedtxt)
## d1 d2
## 9 9
ntype(parsedtxt)
## d1 d2
## 9 9Conversion of tokens is easily performed, and the tokenizers in spacyr tend to be smarter than the purely syntactic pattern-based parsers used by quanteda.
spacy_initialize(model = "en_core_web_sm")
## successfully initialized (spaCy Version: 3.7.2, language model: en_core_web_sm)
parsedtxt <- spacy_parse(txt, pos = TRUE, tag = TRUE)
as.tokens(parsedtxt)
## Tokens consisting of 2 documents.
## d1 :
## [1] "spaCy" "is" "great" "at" "fast"
## [6] "natural" "language" "processing" "."
##
## d2 :
## [1] "Mr." "Smith" "spent" "two" "years" "in" "North"
## [8] "Carolina" "."
as.tokens(parsedtxt, include_pos = "pos")
## Tokens consisting of 2 documents.
## d1 :
## [1] "spaCy/INTJ" "is/AUX" "great/ADJ" "at/ADP"
## [5] "fast/ADJ" "natural/ADJ" "language/NOUN" "processing/NOUN"
## [9] "./PUNCT"
##
## d2 :
## [1] "Mr./PROPN" "Smith/PROPN" "spent/VERB" "two/NUM"
## [5] "years/NOUN" "in/ADP" "North/PROPN" "Carolina/PROPN"
## [9] "./PUNCT"
as.tokens(parsedtxt, include_pos = "tag")
## Tokens consisting of 2 documents.
## d1 :
## [1] "spaCy/UH" "is/VBZ" "great/JJ" "at/IN"
## [5] "fast/JJ" "natural/JJ" "language/NN" "processing/NN"
## [9] "./."
##
## d2 :
## [1] "Mr./NNP" "Smith/NNP" "spent/VBD" "two/CD" "years/NNS"
## [6] "in/IN" "North/NNP" "Carolina/NNP" "./."The latter is useful for say, selecting only nouns, using “glob”
pattern matching with quanteda’s
tokens_select() function:
spacy_parse("The cat in the hat ate green eggs and ham.", pos = TRUE) %>%
as.tokens(include_pos = "pos") %>%
tokens_select(pattern = c("*/NOUN"))
## Tokens consisting of 1 document.
## text1 :
## [1] "cat/NOUN" "hat/NOUN" "eggs/NOUN"Direct conversion of just the spaCy-based tokens is also possible:
spacy_tokenize(txt) %>%
as.tokens()
## Tokens consisting of 2 documents.
## d1 :
## [1] "spaCy" "is" "great" "at" "fast"
## [6] "natural" "language" "processing" "."
##
## d2 :
## [1] "Mr." "Smith" "spent" "two" "years" "in" "North"
## [8] "Carolina" "."including for sentences, for which spaCy’s recognition is very smart:
txt2 <- "A Ph.D. in Washington D.C. Mr. Smith went to Washington."
spacy_tokenize(txt2, what = "sentence") %>%
as.tokens()
## Tokens consisting of 1 document.
## text1 :
## [1] "A Ph.D. in Washington D.C. Mr. Smith went to Washington."This also works well with entity recognition, e.g.
If you prefer a tidy approach to text analysis, spacyr works nicely because it returns parsed texts and (optionally) tokenized texts as data.frame-based objects.
if (!requireNamespace("tidytext", quietly = TRUE))
install.packages("tidytext", repos = "https://cran.rstudio.com/")
library("tidytext")
unnest_tokens(parsedtxt, word, token) %>%
dplyr::anti_join(stop_words)
## Joining with `by = join_by(word)`
## doc_id sentence_id token_id lemma pos tag entity word
## 1 d1 1 1 spacy INTJ UH spacy
## 2 d1 1 5 fast ADJ JJ fast
## 3 d1 1 6 natural ADJ JJ natural
## 4 d1 1 7 language NOUN NN language
## 5 d1 1 8 processing NOUN NN processing
## 6 d2 1 2 Smith PROPN NNP PERSON_B smith
## 7 d2 1 3 spend VERB VBD spent
## 8 d2 1 7 North PROPN NNP GPE_B north
## 9 d2 1 8 Carolina PROPN NNP GPE_I carolinaPart of speech filtering can then happen using dplyr:
spacyr’s output was designed to conform to the Text Interchange Format,
a cooperatively agreed standard structure for text package objects in R,
such as corpus and token objects. spacy_initialize() can
take a TIF corpus data.frame or character object as a valid input.
Moreover, the data.frames returned by spacy_parse() and
entity_consolidate() conform to the TIF tokens standard for
data.frame tokens objects. This will make it easier to use with any text
analysis package for R that works with TIF standard objects.
When spacy_initialize() is executed, a background
process of spaCy is attached in python space. This can take up a
significant size of memory especially when a larger language model is
used (e.g. en_core_web_lg).
When you do not need the connection to spaCy any longer, you can remove
the spaCy object by calling the spacy_finalize()
function.
By calling spacy_initialize() again, you can reattach
the backend spaCy.