mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 05:37:03 +03:00
56 lines
2.5 KiB
Plaintext
56 lines
2.5 KiB
Plaintext
|
//- 💫 DOCS > API > ANNOTATION > TEXT PROCESSING
|
|||
|
|
|
|||
|
|
+aside-code("Example").
|
|||
|
|
from spacy.lang.en import English
|
|||
|
|
nlp = English()
|
|||
|
|
tokens = nlp('Some\nspaces and\ttab characters')
|
|||
|
|
tokens_text = [t.text for t in tokens]
|
|||
|
|
assert tokens_text == ['Some', '\n', 'spaces', ' ', 'and',
|
|||
|
|
'\t', 'tab', 'characters']
|
|||
|
|
|
|||
|
|
p
|
|||
|
|
| Tokenization standards are based on the
|
|||
|
|
| #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
|
|||
|
|
| The tokenizer differs from most by including
|
|||
|
|
| #[strong tokens for significant whitespace]. Any sequence of
|
|||
|
|
| whitespace characters beyond a single space (#[code ' ']) is included
|
|||
|
|
| as a token. The whitespace tokens are useful for much the same reason
|
|||
|
|
| punctuation is – it's often an important delimiter in the text. By
|
|||
|
|
| preserving it in the token output, we are able to maintain a simple
|
|||
|
|
| alignment between the tokens and the original string, and we ensure
|
|||
|
|
| that #[strong no information is lost] during processing.
|
|||
|
|
|
|||
|
|
+h(3, "lemmatization") Lemmatization
|
|||
|
|
|
|||
|
|
+aside("Examples")
|
|||
|
|
| In English, this means:#[br]
|
|||
|
|
| #[strong Adjectives]: happier, happiest → happy#[br]
|
|||
|
|
| #[strong Adverbs]: worse, worst → badly#[br]
|
|||
|
|
| #[strong Nouns]: dogs, children → dog, child#[br]
|
|||
|
|
| #[strong Verbs]: writes, wirting, wrote, written → write
|
|||
|
|
|
|||
|
|
|
|||
|
|
p
|
|||
|
|
| A lemma is the uninflected form of a word. The English lemmatization
|
|||
|
|
| data is taken from #[+a("https://wordnet.princeton.edu") WordNet].
|
|||
|
|
| Lookup tables are taken from
|
|||
|
|
| #[+a("http://www.lexiconista.com/datasets/lemmatization/") Lexiconista].
|
|||
|
|
| spaCy also adds a #[strong special case for pronouns]: all pronouns
|
|||
|
|
| are lemmatized to the special token #[code -PRON-].
|
|||
|
|
|
|||
|
|
+infobox("About spaCy's custom pronoun lemma", "⚠️")
|
|||
|
|
| Unlike verbs and common nouns, there's no clear base form of a personal
|
|||
|
|
| pronoun. Should the lemma of "me" be "I", or should we normalize person
|
|||
|
|
| as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
|
|||
|
|
| novel symbol, #[code -PRON-], which is used as the lemma for
|
|||
|
|
| all personal pronouns.
|
|||
|
|
|
|||
|
|
+h(3, "sentence-boundary") Sentence boundary detection
|
|||
|
|
|
|||
|
|
p
|
|||
|
|
| Sentence boundaries are calculated from the syntactic parse tree, so
|
|||
|
|
| features such as punctuation and capitalisation play an important but
|
|||
|
|
| non-decisive role in determining the sentence boundaries. Usually this
|
|||
|
|
| means that the sentence boundaries will at least coincide with clause
|
|||
|
|
| boundaries, even given poorly punctuated text.
|