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

Which of the following is the correct way to access the part-of-speech (POS) tag for a token using spaCy?


Which of the following is the correct way to access the part-of-speech (POS) tag for a token using spaCy?

```
This PRON	

is AUX

a DET

simple ADJ

sentence NOUN

. PUNCT


```
This PRON

is AUX

a DET

simple ADJ

sentence NOUN


```
This DT

is VBZ

a DT

simple JJ

sentence NN

. .


```
This DT

is VBZ

a DT

simple JJ

sentence NN


What will be the output of the following code? 
``` python
import spacy
nlp = spacy.load("en_core_web_md")
doc = nlp("This is a simple sentence.")
for token in doc:
    print(token.text, token.pos_)


What will be the output of the following code?
<pre><code class="language-python">import spacy
nlp = spacy.load(&quot;en_core_web_md&quot;)
doc = nlp(&quot;This is a simple sentence.&quot;)
for token in doc:
 print(token.text, token.pos_)
</code></pre>

```
This PRON	

is AUX

a DET

simple ADJ

sentence NOUN

. PUNCT

What will be the output of the following code? 
``` python
import spacy
nlp = spacy.load("en_core_web_md")
doc = nlp("This is a simple sentence.")
for token in doc:
    print(token.text, token.tag_)


What will be the output of the following code?
<pre><code class="language-python">import spacy
nlp = spacy.load(&quot;en_core_web_md&quot;)
doc = nlp(&quot;This is a simple sentence.&quot;)
for token in doc:
 print(token.text, token.tag_)
</code></pre>

`nouns = [token for token in doc if token.pos_ == “NOUN”]`

`nouns = [token for token in doc if token.pos_ == “NN”]`


`nouns = [token for token in doc if token.tag_ == “NOUN”]`


`nouns = [token for token in doc if token.pos == “NOUN”]`


Which of the following is the correct way to filter out only the nouns from a spaCy Doc object using POS tagging?


Which of the following is the correct way to filter out only the nouns from a spaCy Doc object using POS tagging?

“How”, "much”, “wood”, “would”, “a”, “woodchuck”, “chuck”, “if”, “a”, “woodchuck”, “would”, “chuck”, “wood”, “?”


“How”, "much”, “wood”, “would”, “a”, “woodchuck”, “chuck”, “if”, “a”, “woodchuck”, “would”, “chuck”, “wood”


“wood”, “woodchuck”, “chuck”, “woodchuck”, “wood”

 What will be the output of the following code? 

``` python
nlp = spacy.load("en_core_web_md")
doc = nlp("How much wood would a woodchuck chuck if a woodchuck would chuck wood?")
nouns = [token for token in doc if token.pos_ == "NOUN"]
for noun in nouns:
    print(noun.text)


What will be the output of the following code?
<pre><code class="language-python">nlp = spacy.load(&quot;en_core_web_md&quot;)
doc = nlp(&quot;How much wood would a woodchuck chuck if a woodchuck would chuck wood?&quot;)
nouns = [token for token in doc if token.pos_ == &quot;NOUN&quot;]
for noun in nouns:
 print(noun.text)
</code></pre>

Let's test what you've learned in this chapter.


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: Linguistic Features