What is segmentation with respect to text data in Tensorflow?

Segmentation refers to the process of splitting text into word-like units. This is essential for natural language processing, especially for languages like Chinese and Japanese that don't use spaces to separate words, or languages like German that contain long compound words requiring segmentation for proper analysis.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

Unicode and Text Processing

Models processing natural language must handle different character sets from various languages. Unicode serves as the standard encoding system, representing characters from almost all languages using unique integer code points between 0 and 0x10FFFF.

TensorFlow provides tools to work with Unicode strings and perform segmentation based on script detection. Let's explore how to process multilingual text ?

Example: Unicode Script Detection

import tensorflow as tf

print("Below is the sentence that is processed")
sentence_texts = [u'Hello, there.', u'???????']

print("The code point values for characters in the sentence")
sentence_char_codepoint = tf.strings.unicode_decode(sentence_texts, 'UTF-8')
print(sentence_char_codepoint)

print("The unicode script values for characters in the sentence")
sentence_char_script = tf.strings.unicode_script(sentence_char_codepoint)
print(sentence_char_script)

Below is the sentence that is processed
The code point values for characters in the sentence
<tf.RaggedTensor: shape=(2, None), dtype=int32, ragged_rank=1>

The unicode script values for characters in the sentence
<tf.RaggedTensor [[25, 25, 25, 25, 25, 0, 0, 25, 25, 25, 25, 25, 0], [17, 17, 20, 20, 20, 20, 20]]>

How Script-Based Segmentation Works

The script detection approach works by identifying character script boundaries. Here's a practical example of segmenting mixed-script text ?

import tensorflow as tf

# Mixed script text: English + Japanese
mixed_text = "NY??"
print(f"Original text: {mixed_text}")

# Decode to Unicode codepoints
codepoints = tf.strings.unicode_decode(mixed_text, 'UTF-8')
print(f"Codepoints: {codepoints}")

# Get script for each character
scripts = tf.strings.unicode_script(codepoints)
print(f"Scripts: {scripts}")

# Find script boundaries for segmentation
script_changes = tf.not_equal(scripts[:-1], scripts[1:])
segment_starts = tf.concat([[True], script_changes], axis=0)
print(f"Segment boundaries: {segment_starts}")

Original text: NY??
Codepoints: [78 89 26666 20385]
Scripts: [25 25 17 17]
Segment boundaries: [ True False  True False]

Key Points

Common Use Cases

• Preprocessing: Segmenting text before tokenization
• Multilingual NLP: Handling mixed-language documents
• Web text processing: Processing international web content
• Script detection: Identifying language boundaries in text

Conclusion

Text segmentation in TensorFlow uses Unicode script detection to split multilingual text into meaningful units. This approach works well for mixed-script content and provides a foundation for further NLP processing without requiring complex ML models.