Gain insights into text preprocessing with Python. Explore text cleaning, normalization, and advanced techniques like BoW and TF-IDF. Discover skills to handle unstructured data effectively for NLP.

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Text Processing

This course is designed to empower you with essential skills for effectively handling text data in the context of natural language processing (NLP). You’ll embark on a transformative journey that will equip you with a solid foundation in text manipulation, enabling you to tackle the challenges of unstructured data. 

The course discusses both fundamental and advanced text preprocessing techniques. You’ll learn how to clean text and remove noise, irrelevant characters, and inconsistencies in text data. Once the data is ready for analysis, you’ll learn text normalization techniques such as stemming, lemmatization, and casing. In addition to mastering preprocessing fundamentals, you’ll also learn techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF).

By the end of the course, you’ll be able to position yourself for success in a data-centric world where the ability to extract meaning from unstructured textual information is a prized skill.

Text Preprocessing with Python

To analyze text data in the order it appears

To generate grammatically correct sentences from text data

To identify the emotional tone of text data

To represent text data numerically for machine-learning algorithms

What's the primary purpose of using the BoW technique in natural language processing?

What’s the primary purpose of using the BoW technique in natural language processing?

Varying weighting based on word importance

Equal weighting regardless of word importance or rarity

What type of weighting does the BoW technique assign to all words?

What type of weighting does the BoW technique assign to all words?

By counting the total words in the corpus

By counting the number of times the term appears in a document

By calculating the logarithm of the ratio of total documents to documents with the term

By measuring the frequency of the term across the entire corpus

How is the term frequency (TF) of a term calculated?

How is the term frequency (TF) of a term calculated?

TF-IDF assigns higher weights to rare terms in the document.

TF-IDF captures more information about the importance of words.

TF-IDF only works with short text documents.

TF-IDF ensures all words are treated equally.

What advantage does TF-IDF offer over the BoW technique?

What advantage does TF-IDF offer over the BoW technique?

It forms the foundation for generating meaningful word embeddings.

It defines the rules of word embedding generation.

It provides a list of words to be removed from the text data.

It enables the removal of stopwords from the text data.

What's the primary role of the vocabulary in word embeddings?

What’s the primary role of the vocabulary in word embeddings?

Consider a corpus of five documents: document A, document B, document C, document D, and document E. The term "cake" appears 8 times in document A, 4 times in document B, 2 times in document C, 1 time in document D, and 0 times in document E. The total number of documents in the corpus is 5. Calculate the TF-IDF score for the term "cake" in document C.

Consider a corpus of five documents: document A, document B, document C, document D, and document E. The term “cake” appears 8 times in document A, 4 times in document B, 2 times in document C, 1 time in document D, and 0 times in document E. The total number of documents in the corpus is 5. Calculate the TF-IDF score for the term “cake” in document C.

[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
  1, 1]

[2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 
  1, 1]

[2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 
  1, 1]

[2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 
  1, 1]

Consider the following document: "The weather is good today. Today is a great day to go outside and enjoy the weather" with the following tokens:
```
["The", "weather", "is", "good", "today", "a", "great", "day", "to", "go", "outside", "and", "enjoy"]
```
What is the BoW representation for this document?

Consider the following document: “The weather is good today. Today is a great day to go outside and enjoy the weather” with the following tokens:
<pre><code>[&quot;The&quot;, &quot;weather&quot;, &quot;is&quot;, &quot;good&quot;, &quot;today&quot;, &quot;a&quot;, &quot;great&quot;, &quot;day&quot;, &quot;to&quot;, &quot;go&quot;, &quot;outside&quot;, &quot;and&quot;, &quot;enjoy&quot;]
</code></pre>
What is the BoW representation for this document?

Test yourself on what you’ve learned about text representation.

About This Course

Introduction To Text Preprocessing

Regular Expressions

Irrelevant Text Data

Basic Text Preprocessing Techniques

Indexing

Text Transformation

Text Representation

Text Feature Engineering

Advanced Text Preprocessing

N-grams

Text Classification of Customer Reviews

Conclusion

Text Classification Using PyTorch

Quiz: Text Representation