[AI from Scratch] Episode 242: Text Data Preprocessing

Recap and Today’s Theme

Hello! In the previous episode, we learned about the basics and applications of Natural Language Processing (NLP). NLP technology processes text and speech data for various applications, including search engines, chatbots, and machine translation.

This time, we will explore an essential step in NLP: text data preprocessing. Preprocessing text data forms the foundation for NLP models to function accurately. It includes techniques such as tokenization, stopword removal, part-of-speech tagging, and creating n-grams. Let’s delve into these methods in detail.

What is Text Data Preprocessing?

1. Importance of Preprocessing

Text data, in its raw form, is difficult for NLP models to handle, making preprocessing necessary. Preprocessing refers to a series of steps that transform text data into a format that is easier to analyze. This includes noise removal, splitting data into words or characters, and data normalization. Proper preprocessing can significantly enhance the accuracy of NLP models.

Details of Tokenization

1. What is Tokenization?

Tokenization is the process of breaking down text into smaller units called tokens. Tokens are the fundamental elements of text, such as words, subwords, or characters. Tokenization is the first step in NLP preprocessing, and breaking text into appropriate units facilitates analysis and model learning.

2. Methods of Tokenization

There are several tokenization methods, and choosing the appropriate one depends on the language and application.

a. Word-Level Tokenization

Word-level tokenization splits text into individual words. For languages like English, where words are separated by spaces, this process is relatively simple. However, for languages like Japanese or Chinese, where words are not separated by spaces, additional processing is required. For example, the Japanese sentence “私は学生です” is split into “私” (I), “は” (topic marker), “学生” (student), and “です” (is/am).

b. Subword-Level Tokenization

Subword-level tokenization breaks words into even smaller units called subwords. For instance, the word “unbelievable” can be split into “un”, “believ”, and “able”. This method effectively handles unknown words and is widely used in advanced language models like BERT and GPT-2, utilizing algorithms such as Byte Pair Encoding (BPE) and WordPiece.

c. Character-Level Tokenization

Character-level tokenization splits text into individual characters. For example, the word “cat” becomes “c”, “a”, and “t”. This method is very flexible and useful when subword or word-level tokenization is challenging. However, it tends to generate long sequences, making processing more complex.

3. Impact of Tokenization

The choice of tokenization method significantly influences model performance. For instance, word-level tokenization makes semantic understanding easier but may struggle with a high frequency of unknown words. Subword-level tokenization covers more words but increases the number of tokens. Character-level tokenization offers flexibility but may complicate contextual understanding.

Stopword Removal

1. What are Stopwords?

Stopwords are commonly used words in sentences that carry little information, such as “the”, “is”, and “in” in English, or “は”, “の”, and “が” in Japanese. While these words are important for grammar, they contribute minimally to understanding the content, so they are often removed during model training.

2. Benefits and Impact of Removing Stopwords

Removing stopwords has several advantages:

• Noise Reduction: Eliminates low-information words, streamlining model learning.
• Reduced Computation Costs: Fewer tokens mean lower computational requirements.

However, stopword removal is not always appropriate. For example, in sentiment analysis, stopwords themselves might carry crucial meaning, and not removing them could improve accuracy.

Basics of Part-of-Speech Tagging

1. What is Part-of-Speech Tagging?

Part-of-Speech Tagging (POS Tagging) assigns each word in a sentence its part of speech (e.g., verb, noun, adjective). POS tagging is a critical step in understanding the grammatical structure and meaning of sentences.

2. Methods of POS Tagging

There are two main methods for POS tagging:

• Rule-Based: Uses grammatical rules to assign tags but struggles with complex contexts.
• Machine Learning-Based: Uses training data to learn tagging patterns and tag new sentences. Deep learning methods, such as BERT, achieve high accuracy in tagging.

3. Applications of POS Tagging

POS tagging plays a crucial role in NLP tasks like information extraction, text summarization, and sentiment analysis, as it helps deepen the understanding of sentence structure and meaning.

Creating N-Grams

1. What is an N-Gram?

An N-gram is a sequence of N consecutive words or characters. For instance, in the sentence “I am a student”, the 2-grams (bigrams) are “I am”, “am a”, and “a student”. N-grams are fundamental for capturing context and are widely used in text classification and language modeling.

2. Applications of N-Grams

N-grams are applied in several NLP tasks, such as:

• Text Classification: N-grams as features help capture the characteristics of the text.
• Autocomplete Systems: Used in systems that predict the next word or character based on sequence continuity.
• Language Models: Serve as the foundation for predicting the next word in sentence generation and machine translation.

3. Effects of N-Gram Selection

Smaller N values (e.g., 1-gram) provide frequency information for individual words, while larger N values (e.g., 4-gram) capture more detailed context. However, larger N values may increase data sparsity, making learning more challenging.

Summary

In this episode, we explored key text preprocessing techniques, including tokenization, stopword removal, POS tagging, and n-gram creation. These processes are foundational for NLP, and properly implementing them can significantly improve model accuracy.

Next Episode Preview

Next time, we will discuss morphological analysis, focusing on methods for word segmentation and POS tagging in languages without spaces, such as Japanese. Stay tuned!

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Notes

1. Byte Pair Encoding (BPE): A tokenization algorithm that segments words into subwords.
2. Sparsity: A state where data is sparsely distributed, often a challenge with high-dimensional data in machine learning.
3. Autocomplete: A feature that automatically completes text input, frequently used in search engines and text editors.