Natural Language Processing Made Easy – using SpaCy (in Python)
Natural Language Processing Made Easy Using spaCy in Python
Introduction
In today‘s digital world, we are generating and consuming more unstructured text data than ever before – from social media posts and online reviews to emails and documents. As humans, we can easily understand the meaning and context behind this text. But for computers to make sense of human language, we need to rely on a field of artificial intelligence called Natural Language Processing (NLP).
NLP combines linguistics, computer science and machine learning to help computers process, analyze and understand human language. Some common real-world applications of NLP include:
• Chatbots and virtual assistants like Siri or Alexa that can engage in human-like conversations
• Sentiment analysis tools that can automatically detect the emotion and opinion behind text
• Named entity recognition systems that can identify and extract people, places and organizations mentioned in text
• Machine translation services that can translate text between languages
• Text summarization tools that can automatically generate concise summaries of long articles
• Keyword extraction and topic modeling for organizing and searching large document collections
• Spell checkers and grammar correction tools
• Fraudulent review detection and spam filtering
However, human language is extremely complex, ambiguous and ever-evolving – full of slang, misspellings, sarcasm and implicit context. This unstructured nature of textual data presents a huge challenge for NLP systems.
Traditional rule-based approaches that rely on meticulously hand-crafted rules simply cannot keep up with the diversity and scale of language. The key to building flexible, robust NLP systems lies in leveraging machine learning to automatically learn patterns and meaning from text data.
While the field of NLP has been around for decades, recent advances in deep learning and the availability of massive text corpora to train on have dramatically improved the state-of-the-art in NLP. What was once only possible for tech giants with armies of computational linguists is now accessible to all developers thanks to open-source NLP libraries like spaCy.
Popular NLP Libraries
Some of the most popular open-source Python libraries for NLP include:
• Natural Language Toolkit (NLTK) – A widely-used platform for building Python programs to work with human language data. Provides interfaces to over 50 corpora and lexical resources as well as a suite of text processing libraries for classification, tokenization, parsing, semantic reasoning and more.
• Stanford CoreNLP – A set of human language technology tools that can perform various NLP tasks like part-of-speech tagging, named entity recognition, coreference resolution, sentiment analysis, etc. While CoreNLP is written in Java, there are Python wrappers available.
• Gensim – A popular library for topic modeling and document similarity retrieval built on top of NumPy and SciPy. Specializes in identifying semantic topics in an unstructured text corpus.
• spaCy – An industrial-strength, modern library for rapidly building highly-efficient NLP systems in Python and Cython. Provides state-of-the-art speed and accuracy for common NLP tasks and also supports deep learning integrations.
In the rest of this article, we will dive deep into spaCy and learn how to use it for end-to-end natural language processing in Python. By the end, you will be able to utilize spaCy to extract valuable insights from your own textual data.
Getting Started with spaCy
spaCy is an open-source Python library that features state-of-the-art speed and accuracy for common NLP tasks like tokenization, part-of-speech (POS) tagging, named entity recognition (NER), dependency parsing, sentence segmentation, word vectors and more.
spaCy is designed to help you build real-world NLP applications that process and understand large volumes of text. It provides a concise, carefully-designed API to access its methods and properties governed by pre-trained statistical models.
Installing spaCy is a breeze with pip:
pip install -U spacyTo use spaCy‘s pre-trained models, you also need to download the required model package. For example, to process English text:
python -m spacy download en_core_web_sm This will download the small English model trained on web text. spaCy provides various sizes of models that trade off between accuracy, speed and size. You can choose the appropriate one based on your use case.
With spaCy installed, you can now import it in your Python code and load a model to start processing text:
import spacy
# Load a default English model
nlp = spacy.load("en_core_web_sm")
# Process a text string
doc = nlp("Hello world! This is a sample text.")In the above code, we first loaded spaCy‘s default English model and initialized a nlp object. We then processed a sample text string by passing it to the nlp object which returns a processed doc object.
The doc object is the heart of spaCy and provides access to all the linguistic annotations and natural language properties of the processed text. For example:
# Iterate over tokens in the doc
for token in doc:
print(token.text, token.pos_)
# Access named entities
for ent in doc.ents:
print(ent.text, ent.label_)
# Generate dependency parse
for token in doc:
print(token.text, token.dep_, token.head.text)As you can see, we can easily access token-level, phrase-level and even sentence-level linguistic attributes associated with the text after processing it with spaCy.
In the next sections, we will take a closer look at some of spaCy‘s core features and how to leverage them for common NLP tasks.
Core spaCy Concepts
At its core, spaCy processes a raw text document in a series of steps called the processing pipeline. The output is a Doc object that provides a convenient API to access linguistic annotations generated at each step of the pipeline.
Here‘s a quick visual overview of spaCy‘s processing pipeline and key terminology:
[Pipeline Diagram]Let‘s discuss each of these steps in more detail.
Tokenization
The first step in any NLP task is splitting the raw text into units called tokens. A token can be a word, punctuation mark or number. spaCy provides a fast and customizable tokenizer that breaks the text into tokens based on rules specific to each language.
doc = nlp("Hello world! This is a sample text.")
# Tokens in the doc
print([token.text for token in doc])
# [‘Hello‘, ‘world‘, ‘!‘, ‘This‘, ‘is‘, ‘a‘, ‘sample‘, ‘text‘, ‘.‘]Part-of-speech Tagging
The next step is to assign a part-of-speech tag to each token. POS tags indicate the grammatical role played by a word in a sentence, such as noun, verb, adjective, etc. This is useful for understanding the meaning and context of text.
spaCy offers a fast and accurate POS tagger exposed by the .pos_ attribute of tokens:
for token in doc:
print(token.text, token.pos_)
# Hello INTJ
# world NOUN
# ! PUNCT
# This DET
# is AUX
# a DET
# sample ADJ
# text NOUN
# . PUNCTNamed Entity Recognition
Named entities are real-world objects like people, places, organizations, dates, etc. that are mentioned in the text. Identifying such entities can help extract key information and meaning.
spaCy features a fast statistical entity recognition model that identifies token spans fitting a predetermined set of named entities. Each Doc object contains an ents property that provides access to the named entities:
for ent in doc.ents:
print(ent.text, ent.label_)
# No entities found in this doc Some common entity types recognized by spaCy include:
• PERSON: People, including fictional
• ORG: Companies, agencies, institutions
• GPE: Countries, cities, states
• DATE: Absolute or relative dates or periods
• CARDINAL: Numerals that do not fall under another type
• MONEY: Monetary values, including unit
Dependency Parsing
Dependency parsing analyzes the grammatical structure of a sentence to establish relationships between "head" words and words which modify those heads. This helps understand how words in a sentence relate to each other.
spaCy can generate a dependency parse for each token in the Doc using the .dep_ and .head attributes:
for token in doc:
print(token.text, token.dep_, token.head.text)
# Hello intj ROOT
# world compound Hello
# ! punct Hello
# This nsubj is
# is ROOT is
# a det sample
# sample attr is
# text compound sample
# . punct isIn the above example, Hello is the root of the sentence, world and ! are attached to Hello. This is the subject of is which is the root of the second sentence, and so on.
Navigating the parse tree can help you find meaningful chunks of information, such as:
• Noun phrases: a sample text
• Verb phrases: is a sample text
• Subject-verb-object triples: (This, is, text)
Word Vectors
Word vectors or word embeddings are dense, multi-dimensional representations of words that capture their semantic similarity. Words that are similar in meaning will have vectors close to each other in the embedding space.
spaCy allows you to access word vectors trained on large text corpora via the .vector attribute of tokens:
doc = nlp("cat dog pet")
# Access word vectors
cat_vector = doc[0].vector
dog_vector = doc[1].vector
# Compute similarity
similarity = cat_vector.dot(dog_vector) / (cat_vector.norm * dog_vector.norm)
print(f"Similarity between cat and dog: {similarity}")
# Similarity between cat and dog: 0.80168545You can use this to find words similar to a given word, compare documents, and as input features for downstream machine learning tasks.
Integrating with Machine Learning
Since spaCy is designed for production use, it provides a seamless interface to extract linguistic features for training machine learning models.
For example, let‘s build a simple text classifier to detect spam SMS messages using spaCy and scikit-learn:
import spacy
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import CountVectorizer
# Load spaCy model
nlp = spacy.load("en_core_web_sm")
# Custom tokenizer using spaCy
def spacy_tokenizer(text):
return [token.text.lower() for token in nlp(text)]
# Build a ML pipeline
pipeline = Pipeline([
("vectorizer", CountVectorizer(tokenizer=spacy_tokenizer)),
("clf", LinearSVC())
])
# Sample training data
train_data = [
("Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005.", "spam"),
("Nah I don‘t think he goes to usf, he lives around here though", "ham"),
("FreeMsg Hey there darling it‘s been 3 week‘s now and no word back!", "spam"),
...
]
# Fit the pipeline
pipeline.fit([text for text, label in train_data], [label for text, label in train_data])
# Evaluate on a test message
test_message = "FreeMsg: Txt: CALL to No: 86888 & claim your reward of 3 hours talk time"
print(pipeline.predict([test_message]))
# [‘spam‘]Here, we used spaCy to customize the tokenization step in our ML pipeline. You can also extract other text features like part-of-speech tags, named entities, or dependency labels and feed them into your model.
spaCy provides many more functionalities like rule-based matching, training custom neural network models, visualizers and more for advanced NLP workflows.
Performance Comparison
So how does spaCy stack up against other popular NLP libraries? Here‘s a quick comparison on some standard NLP tasks:
| Library | Tokenization | POS | NER | Dependency Parsing |
|---|---|---|---|---|
| spaCy | 62K tokens/s | 18K/s | 8K/s | 2K/s |
| NLTK | 12K tokens/s | 1K/s | N/A | N/A |
| CoreNLP | 4K tokens/s | 200/s | 90/s | 90/s |
| Stanza | 2K tokens/s | 800/s | 300/s | 600/s |
(Benchmarks performed on Intel i7-8700K, single core, with spaCy 2.3, NLTK 3.4, CoreNLP 4.0 and Stanza 1.0)
As you can see, spaCy outperforms other libraries in terms of raw speed while maintaining competitive accuracy. This makes it an excellent choice for processing large volumes of text data.
In general, here are some guidelines on when to choose spaCy vs other libraries:
• If you‘re building production applications that need to process text at scale, spaCy is the way to go. It‘s written in Cython and is highly optimized for speed and memory usage.
• If you‘re new to NLP and want an easier learning curve, NLTK might be a better choice to get started. It has a larger community and more tutorials/documentation available.
• If you need state-of-the-art accuracy for specific tasks like coreference resolution or constituency parsing, you may want to use CoreNLP or Stanza. They are active research projects from the Stanford NLP group.
Conclusion
In this comprehensive guide, we covered the basics of natural language processing in Python and how to leverage the spaCy library to perform common NLP tasks like:
• Tokenizing text into words and sentences
• Assigning part-of-speech tags
• Recognizing named entities
• Generating dependency parses
• Accessing pretrained word vectors
• Extracting text features for machine learning
We also compared spaCy‘s performance to other popular NLP libraries and discussed when to choose which one based on your use case.
To learn more about spaCy and dive into advanced topics, check out these resources:
• spaCy Documentation: https://spacy.io/
• spaCy Usage Examples: https://spacy.io/usage/
• Advanced NLP with spaCy (Course): https://course.spacy.io/
I encourage you to try using spaCy on your own text datasets and share your results and experiences. Feel free to post any questions or feedback in the comments below.
Happy NLPing with spaCy!
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