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Introduction to NLP
course content

Course Content

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
NLP and Its ApplicationsUnderstanding Text PreprocessingTokenizationChallenge: Tokenizing a Sentence Tokenization Using Regular ExpressionsChallenge: Tokenizing Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice
2. Stemming and Lemmatization
StemmingChallenge: Stemming the TokensLemmatizationPart of Speech TaggingLemmatization with POS Tagging
3. Basic Text Models
Overview of Vector Space ModelsKey Types of Vector Space ModelsBag of WordsCustomizing Bag of WordsChallenge: Creating a Bag of WordsTF-IDFImplementing TF-IDF
4. Word Embeddings
Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecChallenge: Creating Word Embeddings

bookPart of Speech Tagging

Understading POS Tagging

We have mentioned in the previous chapter that part of speech tagging is beneficial for lemmatization, which is its primary role in text preprocessing, so let's discuss this process in more detail here.

However, using the full lexical terms can become quite cumbersome, especially with large corpus, short representations known as tags are used instead. For example, "VB" instead of verb. In practice, however, different POS taggers may use a bit different tags and more detailed tags like "VBD" for verbs in past tense.

POS with NLTK

In order to perform part of speech tagging with NLTK, you should import the pos_tag() function directly from nltk and apply it on the list of strings (tokens) by passing it as the argument.

Here is an example:


              
123456789101112

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
print(tagged_tokens)
copy

As you can see, this function returns a list of tuples each containing a token and its tag. The line nltk.download('averaged_perceptron_tagger_eng') initiates the download of the dataset and models necessary for the PerceptronTagger, which is the default POS tagger used by NLTK. This includes the trained model based on the averaged perceptron algorithm, enabling users to perform POS tagging on text data.

For better visual representation, we could convert the result to a pandas DataFrame:


              
1234567891011121314

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
import pandas as pd
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
# Convert to DataFrame
print(pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']).T)
copy

Alternatively, we could use pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']) without transposing the DataFrame, so that each row represents a token-tag pair.

Task
test

Swipe to show code editor

Your task is to perform part of speech tagging on the tokens of the text string and convert the resulting list of tuples to a DataFrame.

  1. Import the correct function for POS tagging.
  2. Download the model needed for NLTK's POS tagging.
  3. Perform POS tagging.
  4. Convert the result to DataFrame (do NOT transpose it).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 4
toggle bottom row

bookPart of Speech Tagging

Understading POS Tagging

We have mentioned in the previous chapter that part of speech tagging is beneficial for lemmatization, which is its primary role in text preprocessing, so let's discuss this process in more detail here.

However, using the full lexical terms can become quite cumbersome, especially with large corpus, short representations known as tags are used instead. For example, "VB" instead of verb. In practice, however, different POS taggers may use a bit different tags and more detailed tags like "VBD" for verbs in past tense.

POS with NLTK

In order to perform part of speech tagging with NLTK, you should import the pos_tag() function directly from nltk and apply it on the list of strings (tokens) by passing it as the argument.

Here is an example:


              
123456789101112

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
print(tagged_tokens)
copy

As you can see, this function returns a list of tuples each containing a token and its tag. The line nltk.download('averaged_perceptron_tagger_eng') initiates the download of the dataset and models necessary for the PerceptronTagger, which is the default POS tagger used by NLTK. This includes the trained model based on the averaged perceptron algorithm, enabling users to perform POS tagging on text data.

For better visual representation, we could convert the result to a pandas DataFrame:


              
1234567891011121314

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
import pandas as pd
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
# Convert to DataFrame
print(pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']).T)
copy

Alternatively, we could use pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']) without transposing the DataFrame, so that each row represents a token-tag pair.

Task
test

Swipe to show code editor

Your task is to perform part of speech tagging on the tokens of the text string and convert the resulting list of tuples to a DataFrame.

  1. Import the correct function for POS tagging.
  2. Download the model needed for NLTK's POS tagging.
  3. Perform POS tagging.
  4. Convert the result to DataFrame (do NOT transpose it).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 4
toggle bottom row

bookPart of Speech Tagging

Understading POS Tagging

We have mentioned in the previous chapter that part of speech tagging is beneficial for lemmatization, which is its primary role in text preprocessing, so let's discuss this process in more detail here.

However, using the full lexical terms can become quite cumbersome, especially with large corpus, short representations known as tags are used instead. For example, "VB" instead of verb. In practice, however, different POS taggers may use a bit different tags and more detailed tags like "VBD" for verbs in past tense.

POS with NLTK

In order to perform part of speech tagging with NLTK, you should import the pos_tag() function directly from nltk and apply it on the list of strings (tokens) by passing it as the argument.

Here is an example:


              
123456789101112

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
print(tagged_tokens)
copy

As you can see, this function returns a list of tuples each containing a token and its tag. The line nltk.download('averaged_perceptron_tagger_eng') initiates the download of the dataset and models necessary for the PerceptronTagger, which is the default POS tagger used by NLTK. This includes the trained model based on the averaged perceptron algorithm, enabling users to perform POS tagging on text data.

For better visual representation, we could convert the result to a pandas DataFrame:


              
1234567891011121314

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
import pandas as pd
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
# Convert to DataFrame
print(pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']).T)
copy

Alternatively, we could use pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']) without transposing the DataFrame, so that each row represents a token-tag pair.

Task
test

Swipe to show code editor

Your task is to perform part of speech tagging on the tokens of the text string and convert the resulting list of tuples to a DataFrame.

  1. Import the correct function for POS tagging.
  2. Download the model needed for NLTK's POS tagging.
  3. Perform POS tagging.
  4. Convert the result to DataFrame (do NOT transpose it).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Understading POS Tagging

We have mentioned in the previous chapter that part of speech tagging is beneficial for lemmatization, which is its primary role in text preprocessing, so let's discuss this process in more detail here.

However, using the full lexical terms can become quite cumbersome, especially with large corpus, short representations known as tags are used instead. For example, "VB" instead of verb. In practice, however, different POS taggers may use a bit different tags and more detailed tags like "VBD" for verbs in past tense.

POS with NLTK

In order to perform part of speech tagging with NLTK, you should import the pos_tag() function directly from nltk and apply it on the list of strings (tokens) by passing it as the argument.

Here is an example:


              
123456789101112

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
print(tagged_tokens)
copy

As you can see, this function returns a list of tuples each containing a token and its tag. The line nltk.download('averaged_perceptron_tagger_eng') initiates the download of the dataset and models necessary for the PerceptronTagger, which is the default POS tagger used by NLTK. This includes the trained model based on the averaged perceptron algorithm, enabling users to perform POS tagging on text data.

For better visual representation, we could convert the result to a pandas DataFrame:


              
1234567891011121314

            
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import nltk
import pandas as pd
nltk.download('punkt_tab')
# Download the model needed for NLTK's POS tagging
nltk.download('averaged_perceptron_tagger_eng')
text = "One of the key NLP tasks is part of speech tagging"
text = text.lower()
tokens = word_tokenize(text)
# Perform POS tagging
tagged_tokens = pos_tag(tokens)
# Convert to DataFrame
print(pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']).T)
copy

Alternatively, we could use pd.DataFrame(tagged_tokens, columns=['Token', 'POS tag']) without transposing the DataFrame, so that each row represents a token-tag pair.

Task
test

Swipe to show code editor

Your task is to perform part of speech tagging on the tokens of the text string and convert the resulting list of tuples to a DataFrame.

  1. Import the correct function for POS tagging.
  2. Download the model needed for NLTK's POS tagging.
  3. Perform POS tagging.
  4. Convert the result to DataFrame (do NOT transpose it).

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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