Learn about tokenization, entity recognition, and syntactic parsing using spaCy to apply NLP techniques.

Mastering Natural Language Processing

This module extensively introduces the widely-used Python library, spaCy, for natural language processing (NLP). It covers spaCy basics, such as tokenization and part-of-speech tagging, as well as advanced topics like custom model training and NLP pipeline creation.

This module has three parts. Part 1 focuses on spaCy’s fundamentals, architecture, installation, and setup. It teaches common NLP tasks like tokenization, named entity recognition (NER), part-of-speech (POS) tagging, and dependency parsing.

Part 2 delves into spaCy’s features, covering syntax, and semantics. It explores pattern matching and semantics via word vectors and thoroughly discusses statistical information extraction techniques. In part 3, we’ll examine several advanced topics, such as discussing the development of complex NLP models requiring expertise, analysis, and practical experiences. We will also conduct multiple experiments with various NLP tasks, including customizing statistical models to meet specific needs.

Mastering spaCy

What is the spaCy `Matcher` class used for?


What is the spaCy <code>Matcher</code> class used for?

Given the following code:

```python
import spacy
from spacy.matcher import Matcher
nlp = spacy.load("en_core_web_md")
matcher = Matcher(nlp.vocab)
pattern = [{"LIKE_EMAIL": True}]
matcher.add("EMAIL_ADDRESS", [pattern])
doc = nlp("This is an email address: xyz@abc.com")
matches = matcher(doc)
print (nlp.vocab[matches[0][0]].text)
```
What is the correct option?


Given the following code:
<pre><code class="language-python">import spacy
from spacy.matcher import Matcher
nlp = spacy.load(&quot;en_core_web_md&quot;)
matcher = Matcher(nlp.vocab)
pattern = [{&quot;LIKE_EMAIL&quot;: True}]
matcher.add(&quot;EMAIL_ADDRESS&quot;, [pattern])
doc = nlp(&quot;This is an email address: xyz@abc.com&quot;)
matches = matcher(doc)
print (nlp.vocab[matches[0][0]].text)
</code></pre>
What is the correct option?

It can match both individual tokens and sequences of tokens.

It can only be used for rule-based text classification.

Which option is true about the spaCy `Matcher` class?


Which option is true about the spaCy <code>Matcher</code> class?

The spaCy `Matcher` class is faster and more efficient for large volumes of text.


The spaCy `Matcher` class can match patterns based on linguistic features such as part-of-speech and dependency relationships.


The spaCy `Matcher` class is easier to use for simple pattern-matching tasks.

Regular expressions are more powerful and flexible than `Matcher`.

What is the advantage of using the spaCy `Matcher`class over regular expressions for pattern matching?


What is the advantage of using the spaCy <code>Matcher</code>class over regular expressions for pattern matching?

Which method is used to add a pattern to a spaCy `Matcher` object?

Which method is used to add a pattern to a spaCy <code>Matcher</code> object?

Getting Started

Core Operations with spaCy

Linguistic Features

Rule-Based Matchmaking

Working with Word Vectors and Semantic Similarity

Putting Everything Together: Semantic Parsing with spaCy

Assessment: spaCy Features

Customizing spaCy Models

Text Classification with spaCy

spaCy and Transformers

Putting Everything Together: Designing a Chatbot with spaCy

Appendix

Assessment - Machine Learning with spaCy

Conclusion

Quiz: Rule-Based Matchmaking