Сегментация текста
Алгоритмы поиска границ в тексте: границы графемных кластеров, слов и предложений. Критично для перемещения курсора, выделения текста и обработки.
Boundaries in Unicode Text
Text is not a flat sequence of code points — it has structure. Users think in terms of characters, words, and sentences. But Unicode code points do not map cleanly to these concepts. A single visible character (a grapheme cluster) can span multiple code points. A "word" means different things in English, Japanese, and Arabic. A sentence boundary after a period is ambiguous when periods also appear in abbreviations and numbers.
Unicode Text Segmentation (UAX #29) defines algorithms for finding grapheme cluster boundaries, word boundaries, and sentence boundaries. These algorithms are the foundation for correct cursor movement, text selection, word counting, and spell checking in any Unicode-aware application.
The Grapheme Cluster Problem
Python's len() function counts code points, not user-perceived characters:
# Emoji with ZWJ sequence: 1 visible character, 7 code points
family = "\U0001F468\u200D\U0001F469\u200D\U0001F467\u200D\U0001F466"
print(len(family)) # 7 (code points)
# User sees: 👨👩👧👦 (one family emoji)
# Combining characters
cafe = "cafe\u0301" # e + combining acute = é
print(len(cafe)) # 5 (code points)
print(len("café")) # 4 (precomposed NFC)
# Both render as "café" — 4 user-perceived characters
# Flag emoji: 2 regional indicator symbols = 1 flag
flag = "\U0001F1FA\U0001F1F8" # 🇺🇸
print(len(flag)) # 2 (code points)
# User sees: 🇺🇸 (1 flag)
A grapheme cluster is the minimal unit a user thinks of as a single character. UAX #29 defines grapheme cluster boundary rules that handle: - Base + combining marks - Hangul syllable sequences (jamo combining rules) - Regional indicator pairs (flags) - Zero Width Joiner (ZWJ) sequences (family/profession emoji) - Extend characters (tags, emoji modifiers)
Using UAX #29 in Python
The grapheme package provides UAX #29-compliant grapheme cluster segmentation:
# pip install grapheme
import grapheme
family = "\U0001F468\u200D\U0001F469\u200D\U0001F467\u200D\U0001F466"
print(grapheme.length(family)) # 1
print(list(grapheme.graphemes(family))) # ['👨👩👧👦']
text = "Hello, 世界! 🌍"
print(grapheme.length(text)) # 11 (user-perceived chars)
# Safe string slicing (by grapheme, not code point)
print(grapheme.slice(text, 0, 5)) # 'Hello'
For industrial-strength segmentation including word and sentence boundaries, use ICU via PyICU:
from icu import BreakIterator, Locale
text = "Don't stop. Dr. Smith arrived at 3.14 PM."
bi = BreakIterator.createSentenceInstance(Locale("en_US"))
bi.setText(text)
start = 0
for end in bi:
print(repr(text[start:end]))
start = end
# "Don't stop. " | "Dr. Smith arrived at 3.14 PM."
Quick Facts
| Property | Value |
|---|---|
| Specification | Unicode Standard Annex #29 (UAX #29) |
| Boundary types | Grapheme cluster, word, sentence |
Python len() |
Counts code points, not grapheme clusters |
| Python package | grapheme (pip install grapheme) |
| Full ICU support | PyICU — BreakIterator.createGraphemeInstance() etc. |
| ZWJ sequences | Zero Width Joiner (U+200D) joins emoji into single grapheme cluster |
| Regional indicators | Two regional indicator letters form a single flag grapheme cluster |
| Hangul | Jamo sequences (L + V + T) form a single syllable grapheme cluster |
Связанные термины
Ещё в Алгоритмы
Mapping characters to a common case form for case-insensitive comparison. More comprehensive …
Rules (UAX#29) for determining where one user-perceived character ends and another begins. …
Normalization Form C: декомпозиция с последующей канонической рекомпозицией, дающая кратчайшую форму. Рекомендуется …
Normalization Form D: полная декомпозиция без рекомпозиции. Используется файловой системой macOS HFS+. …
Normalization Form KC: совместимая декомпозиция с последующей канонической композицией. Объединяет визуально похожие …
Normalization Form KD: совместимая декомпозиция без рекомпозиции. Самая агрессивная нормализация с максимальной …
Comparing Unicode strings requires normalization (NFC/NFD) and optionally collation (locale-aware sorting). Binary …
Правила определения мест переноса текста на следующую строку с учетом свойств символов, …
Стандартный алгоритм сравнения и сортировки строк Unicode с многоуровневым сравнением: базовый символ …
Позиция между предложениями по правилам Unicode. Сложнее разделения по точкам — учитывает …