Spacy is introduced as a natural language processing library that is faster and more feature-rich than the popular but older NLTK library. It can tokenize text, identify parts of speech, recognize named entities, and perform syntactic parsing. Spacy uses word vectors and deep learning for tasks like measuring word similarity and is industrial strength, written in Cython for speed and supporting multiple languages so far.

1. spaCy to the rescue
or why NLTK is not cool anymore
Anton Kasyanov | DataRobot

2. Anton Kasyanov | DataRobot
What is spaCy
• Natural language processing library
• Industrial strength - based on latest research
• Fast - written using Cython

3. Anton Kasyanov | DataRobot
Usage
import spacy
nlp = spacy.load(‘en')
doc = nlp(
‘Hello, world.’
‘Here are two sentences.’
)

4. Anton Kasyanov | DataRobot
Tokeniser
token = doc[0]
sentence = next(doc.sents)
assert token is sentence[0]
assert sentence.text ==
‘Hello, world.'

5. Anton Kasyanov | DataRobot
Word Vectors
doc = nlp(“Apples and oranges are similar.
Boots and hippos aren’t.")
apples = doc[0]
oranges = doc[2]
boots = doc[6]
hippos = doc[8]
assert apples.similarity(oranges) >
boots.similarity(hippos)

6. Anton Kasyanov | DataRobot
Syntactic Parser

7. Anton Kasyanov | DataRobot
Speed

8. Anton Kasyanov | DataRobot
Other features
• Part-of-Speech tagger
• Named entities recognition
• Integer IDs for words
• Multi-threading support
• Deep learning
• German, English, French (so far)

9. Anton Kasyanov | DataRobot
Thanks!
https://spacy.io
antonkasyanov.com