How to scale and improve your

NLP pipelines with

​

  • Freelance Senior Data Scientist
  • +7 years experience in Consulting, Tech, Startups
  • Interests in NLP, MLOps, and AI products

Ahmed BESBES

Follow me on

Goals

​In this presentation you will:

  • Get to know spaCy and discover some of its hidden features
  • Perform low-level NLP tasks
  • Speed up processing with state-of-the-art speed
  • Enhance statistical models with rule-based techniques
  • Use visualization to debug models

Don't be shy. Ask questions!

Agenda

 

  1. Introduction to spaCy
  2. Scaling and performance
  3. Rule-based matching with the Matcher class
  4. Custom Named Entity Recognizers with the EntityRuler
  5. Multiple visualizers
  6. Custom components
  7. Open-source projects

 

 

1. Introduction to spaCy

  • Open-source library for advanced Natural Language Processing (NLP) in Python
  • Designed for production use 
  • Used to build information extraction systems and preprocess text for deep learning

 

pip install -U pip setuptools wheel
pip install -U spacy
python -m spacy download en_core_web_sm

Multiple features under the hood

State-of-the-art processing speed

Multiple statistical models

import spacy

nlp = spacy.load("en_core_web_sm")

doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

for token in doc:
    print(token.text, token.pos_, token.dep_)
  Apple PROPN nsubj
  is AUX aux
  looking VERB ROOT
  at ADP prep
  buying VERB pcomp
  U.K. PROPN dobj
  startup NOUN dobj
  for ADP prep
  $ SYM quantmod
  1 NUM compound
  billion NUM pobj

A clean and simple API

A robust processing pipeline

  • A pipeline is composed of multiple components
  • It turns an input text into a Doc object
  • Some components can be removed or deactivated
  • Custom components can be created and added to the pipeline

 

Multiple native components

  • The Doc object is the output of a processing pipeline 
  • It's a list of Token objects
  • Each Token object  stores multiple attributes
  • A Span is is a slice of the Doc object

Doc, Token, Span, ...?

Multiple token attributes

link

import spacy

nlp = spacy.load("en_core_web_sm")

doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

for token in doc:
    print(token.text, token.pos_, token.dep_)
  Apple PROPN nsubj
  is AUX aux
  looking VERB ROOT
  at ADP prep
  buying VERB pcomp
  U.K. PROPN dobj
  startup NOUN dobj
  for ADP prep
  $ SYM quantmod
  1 NUM compound
  billion NUM pobj

Code example #0

2. Scaling and performance

Use nlp.pipe method

Preprocesses texts as a stream, yields Doc objects

Much faster than calling nlp on each texst

 

# BAD

docs = [nlp(text) for text in LOTS_OF_TEXTS]

# GOOD

docs = list(nlp.pipe(LOTS_OF_TEXTS))
import os
import spacy

nlp = spacy.load("en_core_news_sm")

texts = ... # a large list of documents

batch_size = 128

docs = []

for doc in nlp.pipe(texts, n_process=os.cpu_count()-1, batch_size=batch_size):
    docs.append(doc)

spaCy can also leverage multiprocessing and batching

 

Tip #1 to speed up the computation 💡

Disable unused components for the pipeline

import spacy
nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser"])

Tip #2 to speed up the computation 💡

If you want to tokenize the text only, use the nlp.make_doc

# BAD
doc = nlp("Hello World")

# GOOD
doc = nlp.make_doc("Hello World")

3.​ Rule-based matching with the Matcher class​

  • The Matcher class detects a sequence of tokens that match a specific rule
  • Each token must obey a given pattern
  • Patterns rely on token attributes and properties (text, tag_, dep_, lemma_)
  • Operators and properties can be used to create complex patterns

Example of patterns - #1

from spacy.matcher import Matcher

nlp = spacy.load("en_core_web_sm")

matcher = Matcher(nlp.vocab)

pattern = [
   {"TEXT": "Hello"}
]

matcher.add("HelloPattern", [pattern])

doc = nlp("Hello my friend!")
matcher(doc)

>>> [(10496072603676489703, 0, 1)]

match = matcher(doc)
match_id, start, end = match[0]

doc[start:end]

>>> Hello

Example of patterns - #2

matcher = Matcher(nlp.vocab)

pattern = [
  {"LOWER": "hello"}, 
  {"IS_PUNCT": True}, 
  {"LOWER": "world"}
]

matcher.add("HelloWorldPattern", [pattern])

doc = nlp("Hello, world! This is my first attempt using the Matcher class")
matcher(doc)

>>> [(15578876784678163569, 0, 3)]

match = matcher(doc)
match_id, start, end = match[0]

doc[start:end]

>>> Hello, world

Example of patterns - #3

matcher = Matcher(nlp.vocab)

pattern = [
  {"LEMMA": {"IN": ["like", "love"]}},
  {"POS": "NOUN"}
]

matcher.add("like_love_pattern", [pattern])

doc = nlp("I really love pasta!")
matcher(doc)

>>> [(2173185394966972186, 2, 4)]

match = matcher(doc)
match_id, start, end = match[0]

doc[start:end]

>>> love pasta

Example of patterns - #4

pattern = [
  {"LOWER": {"IN": ["iphones", "ipads", "imacs", "macbooks"]}}, 
  {"LEMMA": "be"}, 
  {"POS": "ADV", "OP": "*"}, 
  {"POS": "ADJ"}
]

matcher.add("apple_products", [pattern])
doc = nlp("""Here's what I think about Apple products: Iphones are expensive, 
Ipads are clunky and macbooks are professional.""")

matcher(doc)

>>> [(4184201092351343283, 9, 12),
     (4184201092351343283, 14, 17),
     (4184201092351343283, 18, 21)]

matches = matcher(doc)
for match_id, start, end in matches:
  	print(doc[start:end])

>>> Iphones are expensive
    Ipads are clunky
    macbooks are professional

More patterns - #5

pattern_length = [{"LENGTH": {">=": 10}}]

pattern_email = [{"LIKE_EMAIL": True}]

pattern_url = [{"LIKE_URL": True}]

pattern_digit = [{"IS_DIGIT": True}]

pattern_ent_type = [{"ENT_TYPE": "ORG"}]

pattern_regex = [{"TEXT": {"REGEX": "deff?in[ia]tely"}}]

pattern_bitcoin = [
  {"LEMMA": {"IN": ["buy", "sell"]}}, 
  {"LOWER": {"IN": ["bitcoin", "dogecoin"]}},
]

Why you should use the Matcher class

Extract expressions and noun phrases

Enhance regular expressions with token annotations (tag_, dep_, text, etc.)

A rich syntax

Create complex patterns with operators and properties...

Preannotate data for NER training

 

Try out the interactive online Matcher

4. Custom Named Entity Recognizers with the EntityRuler

  •  
  • spaCy provides multiple Named Entity Recognition models
  • NER models recognize multiple things
    • Persons
    • Organizations
    • Locations

 

 

NER models can also be enhanced by data dictionaries and rules

Allows to combine statistical with rule-based models for more powerful pipelines

 

Useful to detect very specific entities not captured by statistical models

 

New entities are added as patterns in an EntityRuler component

 

import spacy

nlp = spacy.blanc("en")
doc_before = nlp("John lives in Atlanta")

# No entities are detected

print(doc_before.ents)
# ()

# Create an entity ruler and add it some patterns

entity_ruler = nlp.add_pipe("entity_ruler")

patterns = [
    {
        "label": "PERSON",
        "pattern": "John",
        "id": "john",
    },
    {
        "label": "GPE",
        "pattern": [{"LOWER": "atlanta"}],
        "id": "atlanta",
    },
]

entity_ruler.add_patterns(patterns)
doc_after = nlp("Jonh lives in Atlanta.")

for ent in doc.ents:
    print(ent.text, ":", ent.label_)  
# John : PERSON
# atlanta : GPE
import spacy
import scispacy

# load a spacy model that detects DNA, RNA and PROTEINS from
# biomedical documents

model = spacy.load(
    "en_ner_jnlpba_md",
    disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"],
)

# build a list of patterns and inject them into the entity ruler. 
# these patterns contain entities that are not initially captured 
# by the model. 
# knowledge bases or ontologies could be used to construct the patterns

patterns = build_patterns_from_knowledge_base()

print(patterns[:3])
# [{'label': 'PROTEIN', 'pattern': 'tetraspanin-5'},
#  {'label': 'PROTEIN', 'pattern': 'estradiol 17-beta-dehydrogenase akr1b15'},
#  {'label': 'PROTEIN', 'pattern': 'moz, ybf2/sas3, sas2 and tip60 protein 4'}]

# define an entity ruler
entity_ruler = model.add_pipe("entity_ruler", after="ner")

# add the patterns to the entity ruler

Usecase: How to improve the detection of biomedical entities with an EntityRuler?

5. Multiple visualizers (dependencies)

import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Ahmed is a freelance data scientist and works in Paris")

displacy.serve(doc, style="dep")

Also possible from Jupyter

and ... Streamlit

https://github.com/explosion/spacy-streamlit

6. Custom components

A function that takes a doc, modifies it, and returns it

Registered using the Language.component decorator

Added using the nlp.add_pipe method

@Language.component("custom_component")
def custom_component_function(doc):
    # Do something to the doc here
    return doc

nlp.add_pipe("custom_component")

A simple custom component

import spacy
from spacy.language import Language


@Language.component("custom_component")
def custom_component(doc):
  	print(f"Doc length : {len(doc)}")
    return doc


nlp = spacy.load("en_core_web_sm")

nlp.add_pipe("custom_component", first=True)

>>> print("Pipeline:", nlp.pipe_names)
# Pipeline: ['custom_component', 'tok2vec', 'tagger', 'parser', 
# 			 'ner', 'attribute_ruler', 'lemmatizer']

>>> doc = nlp("I love pasta!")
# Doc length: 4

A more complex custom component

import spacy
from spacy.language import Language
from spacy.matcher import PhraseMatcher
from spacy.tokens import Span

nlp = spacy.load("en_core_web_sm")
animals = ["Golden Retriever", "cat", "turtle", "Rattus norvegicus"]
animal_patterns = list(nlp.pipe(animals))
print("animal_patterns:", animal_patterns)
matcher = PhraseMatcher(nlp.vocab)
matcher.add("ANIMAL", animal_patterns)

# Define the custom component
@Language.component("animal_component")
def animal_component_function(doc):
    # Apply the matcher to the doc
    matches = matcher(doc)
    # Create a Span for each match and assign the label "ANIMAL"
    spans = [Span(doc, start, end, label="ANIMAL") for match_id, start, end in matches]
    # Overwrite the doc.ents with the matched spans
    doc.ents = spans
    return doc


# Add the component to the pipeline after the "ner" component
nlp.add_pipe("animal_component", after="ner")
print(nlp.pipe_names)

# Process the text and print the text and label for the doc.ents
doc = nlp("I have a cat and a Golden Retriever")
print([(ent.text, ent.label_) for ent in doc.ents])

7. Open-source projects

Resources

https://spacy.io/

https://ner.pythonhumanities.com/intro.html

https://towardsdatascience.com/7-spacy-features-to-boost-your-nlp-pipelines-and-save-time-9e12d18c3742

https://www.youtube.com/playlist?list=PLBmcuObd5An5DOl2_IkB0JGQTGFHTAP1h

 

Thank you

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