UAX #29: Text Boundaries (original) (raw)

Unicode Standard Annex #29

Summary

_This document describes guidelines for determining default boundaries between certain significant text elements: grapheme clusters (“user characters”), words, and sentences. For line-break boundaries, see UAX #14: Line Breaking Properties.

Status

This document has been reviewed by Unicode members and other interested parties, and has been approved by the Unicode Technical Committee as a Unicode Standard Annex. This is a stable document and may be used as reference material or cited as a normative reference by other specifications.

A Unicode Standard Annex (UAX) forms an integral part of the Unicode Standard, but is published as a separate document. The Unicode Standard may require conformance to normative content in a Unicode Standard Annex, if so specified in the Conformance chapter of that version of the Unicode Standard. The version number of a UAX document corresponds to the version number of the Unicode Standard at the last point that the UAX document was updated.

Please submit corrigenda and other comments with the online reporting form [Feedback]. Related information that is useful in understanding this document is found in References. For the latest version of the Unicode Standard see [Unicode]. For a list of current Unicode Technical Reports see [Reports]. For more information about versions of the Unicode Standard, see [Versions].

Contents

1 Introduction

1.1 Notation

2 Conformance

3 Grapheme Cluster Boundaries

4 Word Boundaries

5 Sentence Boundaries

6 Implementation Notes

6.1 Normalization

6.2 Grapheme Cluster and Format Rules

6.3 Regular Expressions

6.4 Random Access

6.5 Tailoring

7 Testing

Acknowledgements

References

Modifications

1 Introduction

This document describes guidelines for determining default boundaries between certain significant text elements: grapheme clusters (“user characters”), words, and sentences. It updates most of 5.15 Locating Text Element Boundaries except for the line-break boundaries, which are covered in UAX #14: Line Breaking Properties.

A string of Unicode-encoded text often needs to be broken up into text elements programmatically. Common examples of text elements include what users think of as characters, words, lines (more precisely, where line breaks are allowed), and sentences. The precise determination of text elements may vary according to orthographic conventions for a given script or language. The goal of matching user perceptions cannot always be met exactly because the text alone does not always contain enough information to unambiguously decide boundaries. For example, the period (U+002E full stop) is used ambiguously, sometimes for end-of-sentence purposes, sometimes for abbreviations, and sometimes for numbers. In most cases, however, programmatic text boundaries can match user perceptions quite closely, or at least not surprise the user.

Rather than concentrate on algorithmically searching for text elements themselves, a simpler and more useful computation looks instead at detecting the boundaries (or breaks) between those text elements. The determination of those boundaries is often critical to the performance of general software, so it is important to be able to make such a determination as quickly as possible.

The default boundary determination mechanism provides a straightforward and efficient way to determine some of the most significant boundaries in text: grapheme clusters (what end-users usually think of as characters), words, and sentences. (Line boundaries are to be found in UAX #14: Line Breaking Properties.)

Boundary determination builds upon the uniform character representation of the Unicode Standard, while handling the large number of characters and special features such as combining marks and conjoining jamo in an effective manner. As this mechanism lends itself to a completely data-driven implementation, it can be tailored to particular orthographic conventions or user preferences without recoding.

The large character set of the Unicode Standard and its representational power place requirements on both the specification of text element boundaries and the underlying implementation. The specification needs to allow for the designation of large sets of characters sharing the same characteristics (for example, uppercase letters), while the implementation must provide quick access and matches to those large sets. The mechanism also must handle special features of the Unicode Standard, such as combining or nonspacing marks and conjoining jamo.

1.1 Notation

A boundary specification defines boundary property values used in that specification, then lists the rules for boundaries in terms of those property values. The characters having those property values are specified as a list, where each element of the list is:

• A literal character
• A range of literal characters
• All characters satisfying a given condition, using properties defined in the Unicode Character Database [UCD]:
  • Non-boolean property values are given as =, such as General_Category = Titlecase_Letter.
  • Boolean properties are given as =true, such as
    Uppercase = true.
  • Other conditions are specified textually in terms of UCD properties.
• Boolean combinations of the above
• The two special identifiers sot and eot stand for start and end of text respectively.

The rules are numbered for reference and are applied in order in order to determine whether there is a boundary at a given offset. That is, there is an implicit “otherwise” at the front of each rule following the first. So logically the rules are processed from top to bottom. As soon as a rule matches and produces a boundary status (boundary or no-boundary) for that offset, the process is terminated.

Each rule consists of a left side, a boundary symbol, and a right side. Either of the sides can be empty. The left and right sides use the boundary property values in regular expressions. For more information on regular expression syntax used, see UTR #18: Unicode Regular Expression Guidelines.

The boundary symbols are:

An underscore (“_”) is used to indicate a space in examples.

As in other cases, these are a logical description of the processes: implementations can achieve the same results without using code or data that follows these rules step-by-step. In particular, many production-grade implementations will use a state-table approach. In that case, the performance does not depend on the complexity or number of rules. The only feature that does affect performance is the number of characters that may match after the boundary position in a rule that is matched.

Some additional constraints are reflected in the specification. These constraints make the implementation significantly simpler and more efficient and have not been found to be limitations for natural language use. In particular, the rules are designed so that they can be implemented with a deterministic finite-state machine.

1. Single boundaries. Each rule has exactly one boundary position. Because of constraint (1), this restriction is more a limitation on the specification methods, because a rule with two boundaries could generally be expressed as two rules. For example, “ab ÷ cd ÷ ef” could be broken into “ab ÷ cd” and “cd ÷ ef”.
2. Ignore degenerates. No special provisions are made to get marginally better behavior for degenerate cases that never occur in practice, such as an A followed by an Indic combining mark.

Different issues are present with different types of boundaries, as the following discussion and examples should make clear.

2 Conformance

This is informative material. There are many different ways to divide text elements corresponding to grapheme clusters, words and sentences, and the Unicode Standard and this document do not restrict the ways in which implementations can do this.

This specification is a default mechanism; more sophisticated engines can and should tailor it for particular locales or environments. For example, good Thai, Lao, Chinese, or Japanese word-break boundaries require the use of dictionary lookup, analogous to English hyphenation. An implementation therefore may need to provide means to override or subclass the default mechanism described in this document. Note that tailoring can either add boundary positions or remove boundary positions, compared to the default specified here.

To maintain canonical equivalence, all of the following specifications are defined on NFD text, as defined in UAX #15: Unicode Normalization Forms [Reports]. A boundary exists in non-NFD text just in case it would occur at the corresponding position in NFD text. However, this is only a logical specification; in practice implementations can avoid normalization and achieve the same results. For more information, see §6 Implementation Notes.

3 Grapheme Cluster Boundaries

One or more Unicode characters may make up what the user thinks of as a character or basic unit of the language. To avoid ambiguity with the computer use of the term character, this is called a grapheme cluster. For example, “G” + acute-accent is a grapheme cluster: it is thought of as a single character by users, yet is actually represented by two Unicode code points. For more information on the ambiguity in the term character, see UTR #17: Character Encoding Model [Reports].

Grapheme clusters include, but are not limited to, combining character sequences such as (g + °), digraphs such as Slovak “ch”, and sequences with letter modifiers such as kw. Grapheme cluster boundaries are important for collation, regular-expressions, and counting “character” positions within text. Word boundaries, line boundaries and sentence boundaries do not occur within a grapheme cluster. In this section, the Unicode Standard provides a determination of where the default grapheme boundaries fall in a string of characters. This algorithm can be tailored for specific locales or other customizations, which is what is done in providing contracting characters in collation tailoring tables.

Note: In previous documentation, default grapheme clusters were previously referred to as “locale-independent graphemes”. The term cluster has been added to emphasize that the term grapheme as used differently in linguistics. For simplicity and to align with UTS #10: Unicode Collation Algorithm [Reports], the terms default and tailored are used in preference to locale-independent and locale-dependent, respectively.

As far as a user is concerned, the underlying representation of text is not important, but it is important that an editing interface present a uniform implementation of what the user thinks of as characters. Grapheme clusters commonly commonly behave as units in terms of mouse selection, arrow key movement, backspacing, and so on. When this is done, for example, and an accented character is represented by a combining character sequence, then using the right arrow key would skip from the start of the base character to the end of the last combining character.

However, in some cases editing a grapheme cluster element by element may be the preferred way. For example, a system might have backspace delete by code point, while the delete key may delete an entire cluster. Moreover, there is not a one-to-one relationship between grapheme clusters and keys on a keyboard. A single key on a keyboard may correspond to: a whole grapheme cluster, a part of a grapheme clusters, or a sequence of more than one grapheme clusters.

In those rare circumstances where end-users need character counts, the counts should correspond to the grapheme cluster boundaries.

The principal requirements for default grapheme cluster boundaries are the handling of combining marks and Hangul conjoining jamo. Boundaries may be further tailored for requirements of different languages, such as the addition of “ch” for Slovak, or Indic, Thai or Tibetan character clusters.

A default grapheme cluster normally begins with a base character. Exceptions are when a combining mark is at the start of text, or it is preceded by a control (or format) character. In place of a single base character, a Hangul Syllable composed of one or more characters may serve as the base. For the rules defining the default boundaries, see Table 1, Default Grapheme Cluster Boundaries below. For more information on the composition of Hangul Syllables see [Unicode].

Note: The boundary between default grapheme clusters can be determined by just the two adjacent characters. See 7 Testing for a chart showing the interactions of pairs of characters.

Degenerate Cases. As with other definitions in Chapter 5 and elsewhere, these definitions are designed to be simple to implement. They need to provide an algorithmic determination of the valid, default grapheme clusters, and exclude sequences that are normally not considered default grapheme clusters. However, they do not have to catch edge cases that will not occur in practice.

The definition of default grapheme clusters is not meant to exclude the use of more sophisticated definitions of tailored grapheme clusters where appropriate: definitions that more precisely match the user expectations within individual languages. For example, “ch” may be considered a grapheme cluster in Slovak. It is, however, designed to provide a much more accurate match to overall user expectations for characters than is provided by individual Unicode code points.

Display of Grapheme Clusters. Grapheme clusters are not the same as ligatures. For example, the grapheme cluster “ch” in Slovak is not normally a ligature, and conversely, the ligature “fi” is not a grapheme cluster. Default grapheme clusters do not necessarily reflect text display. For example, the sequence <f, i> may be displayed as a single glyph on the screen, but would still be two grapheme clusters.

For more information on the use of grapheme clusters, see UTR #18: Unicode Regular Expression Guidelines [Reports].

Note: As with the other default specifications, implementations are free to override (tailor) the results to meet the requirements of different environments or particular languages.

Table 1. Default Grapheme Cluster Boundaries

Boundary Property Values

Boundary Rules

4 Word Boundaries

Word boundaries are used in a number of different contexts. The most familiar ones are double-click mouse selection, “move to next word” control-arrow keys, and “Whole Word Search” (WWS) for search and replace. They are also used in database queries, to determine whether elements are within a certain number of words of one another.

Word boundaries can also be used in so-called intelligent cut and paste. With this feature, if the user cuts a piece of text on word boundaries, adjacent spaces are collapsed to a single space. For example, cutting “quick” from “The_quick_fox” would leave “The_ _fox”. Intelligent cut and paste collapses this text to “The_fox”.

Here is an example of word boundaries.

Example 1: Word Boundaries | The | | quick | | ( | “ | brown | ” | ) | | fox | | can’t | | jump | | 32.3 | | feet | , | | right | ? | | --- | | ----- | | - | - | ----- | - | - | | --- | | ----- | | ---- | | ---- | | ---- | - | | ----- | - |

There is a boundary, for example, on either side of the word brown. These are the boundaries that users would expect, for example, if they chose WWS. Matching brown with WWS works, since there is a boundary on either side. Matching brow doesn’t. Matching “brown” also works, since there are boundaries between the parentheses and the quotation marks.

For proximity tests, one sees whether, for example, “monster” is within 3 words of “truck”. That is done with the above boundaries by ignoring any words that do not contain a letter, as in Example 2 below. Thus for proximity, “fox” is within three words of “quick”. This same technique can be used for “get next/previous word” commands or keyboard arrow-keys. Letters are not the only characters that can be used to determine the “significant” words: different implementations may include other types of characters such as digits, or perform other analysis of the characters.

Example 2: Extracted Words

The word boundaries are related to the line boundaries, but are distinct: there are some word-break boundaries that are not line-break boundaries and vice-versa. However, it is relatively seldom that a line-break boundary is not a word-break boundary. One example is a word containing a SHY (soft hyphen). It will break across lines, yet is a single word.

Note: As with the other default specifications, implementations are free to override (tailor) the results to meet the requirements of different environments or particular languages.

Table 2. Default Word Boundaries

Boundary Property Values

Boundary Rules

Notes:

• Unfortunately not all issues can be resolved across languages (or even within a language, since there are ambiguities). The goal is to have as workable a default as possible; tailored engines can be more sophisticated about these matters.
• For Thai, Lao, Khmer, Mynmar, and other scripts that don't use typically use spaces between words, a good implementation should not just depend on the default word boundary specification, but should use a more sophisticated mechanism, as in LineBreak. Ideographic scripts such as Japanese and Chinese are even more complex. Where Hangul text is written without spaces, the same applies. However, in the absence of such a more sophisticated mechanism, the above at least supplies a well-defined default.
• The hard hyphen is a tricky case. It is quite common for separate words to be connected with a hyphen: out-of-the-box, under-the-table, Anglo-american, etc. A significant number are hyphenated names: Smith-Hawkins, etc. When people do a “Whole Word” search or query, they expect to find the word within those hyphens. While there are some cases where they are separate words (usually to resolve some ambiguity such as re-sort vs. resort) it’s better overall to keep the hard hyphen out of the default definition.
• Apostrophe is another tricky case. Usually considered part of one word (“can’t”, “aujourd’hui”) it may also be considered two (“l’objectif”). Also, one cannot easily distinguish the cases where it is used as a quotation mark from those where it is used as an apostrophe, so one should not include leading or trailing apostrophes. In some languages, such as French and Italian, tailoring it to break words when the character after the apostrophe is a vowel may yield better results in more cases. This can be done by adding a rule 5a:

  Break between hyphens and vowels (French, Italian)
  hyphens	÷	vowels	(5a)
  and defining appropriate property values for hyphens and vowels.

• Certain cases like colon in words (c:a) are included even though they may be specific to relatively small user communities (Swedish) because they don’t occur otherwise in normal text, and so don’t cause a problem for other languages.
• For Hebrew, a tailoring may include a double quotation mark between letters, since legacy data may contain that in place of U+05F4 (״) gershayim. This can be done by adding double quotation mark to MidLetter.
• Format characters are ignored if they are interior. Thus will break, and will break, but there is no break in .

5 Sentence Boundaries

Sentence boundaries are often used for triple-click or some other method of selecting or iterating through blocks of text that are larger than single words. They are also used to determine whether words occur within the same sentence in database queries.

Plain text provides inadequate information for determining good sentence boundaries. Periods, for example, can either signal the end of a sentence, indicate abbreviations, or be used for decimal points. One cannot, without much more sophisticated analysis, distinguish between the two following cases:

He said, “Are you going?”	John shook his head.

“Are you going?” John asked.

Without analyzing the text semantically, it is impossible to be certain which of these usages is intended (and sometimes ambiguities still remain). However, in a large majority of cases, a straightforward mechanism works well.

Note: As with the other default specifications, implementations are free to override (tailor) the results to meet the requirements of different environments or particular languages.

Table 3. Default Sentence Boundaries

Boundary Property Values

Boundary Rules

Notes:

• Rules 6-8 are designed to forbid breaks within strings like:

  c.d
  3.4
  U.S.
  ... the resp. leaders are ...
  ... etc.)’ ‘(the ...
  while permitting breaks in strings like:
  She said “See spot run.”
  ------------------------
  ... etc.
  ...理数字.
  As discussed above, they cannot detect cases like “Mr. Jones”; more sophisticated tailoring would be required for that.

6 Implementation Notes

6.1 Normalization

Although boundaries are specified in terms of NFD text, in practice normalization is not required. The Grapheme Cluster specification has a number of features to are to ensure that the same results are returned for canonically equivalent text. It will not break within a sequence of non-spacing marks, which is the only part that can reorder in the formation of NFD. Nor is there ever a break between a base character and subsequent non-spacing marks. It also has a special set of characters marked as having the Extend property value, such as U+09BE ( ◌া ) BENGALI VOWEL SIGN AA, to deal with particular compositions.

The other default boundary specifications never break within grapheme clusters, and always use a consistent property value for each grapheme cluster as a whole.

6.2 Grapheme Cluster and Format Rules

The first rule for the default word and sentence specifications is to treat a grapheme cluster as a single character: the first character of the cluster. This would be equivalent to making the following changes to the subsequent rules.

• Add rules 1, 3-4 from Table 1 Default Grapheme Cluster Table to disallow breaking between Hangul syllables or CRLF.
  • Note: neither word nor sentence specifications distinguish between L, V, T, LV, or LVT; nor between CR and LF, nor does it matter whether there is a sequence of these or a single one.
• Insert Extend* after every boundary property value — except after the final property after the break symbol.
  • Thus X Y × Z W becomes
    * X Extend* Y Extend* × Z Extend* W
  • Thus X Y × becomes
    * X Extend* Y Extend* ×

The other special rule for the word and sentence specifications is to ignore interior Format characters. This would be equivalent to making the following further changes to the affected subsequent rules.

• If the break symbol is a no-break sign (×), insert Format* after each Extend*, and after the break symbol — except where there is nothing after the break symbol.
  • Thus X Extend* Y Extend* × Z Extend* W becomes
    * X Extend* Format* Y Extend* Format* × Format* Z Extend* Format* W
  • Thus X Extend* Y Extend* × becomes
    * X Extend* Format* Y Extend* ×
• If the break symbol is a break sign (÷), insert Format* after each Extend* — except not before the break symbol.
  • Thus X Extend* Y Extend* ÷ Z Extend* W becomes
    * X Extend* Format* Y Extend* ÷ Z Extend* Format* W
  • Thus X Extend* Y Extend* ÷ becomes
    * X Extend* Format* Y Extend* ÷

6.3 Regular Expressions

The above rules can be converted into a regular expression that will produce the same results. The regular expression must be evaluated starting at a known boundary (such as the start of the text), and take the longest match (except in the case of Sentence boundaries, where the shortest match needs to be used).

The conversion into a regular expression is fairly straightforward, although it takes a little thought. For example, Table 1. Default Grapheme Cluster Boundaries can be transformed into the following regular expression:

Control
| CR LF
| ( !Control? | L+ | T+ | L* ( LV? V+ | LV | LVT ) T* ) Extend*

Such a regular expression can also be turned into a fast deterministic finite-state machine. For more information on Unicode Regular Expressions, see UTR #18: Unicode Regular Expression Guidelines [Reports].

6.4 Random Access

A further complication is introduced by random access. When iterating through a string from beginning to end, a regular expression / state machine works well. From each boundary to find the next boundary is very fast. By constructing a state table for the reverse direction from the same specification of the rules, reverse iteration is possible.

However, suppose that the user wants to iterate starting at a random point in the text, or detect whether a random point in the text is a boundary. If the starting point does not provide enough context to allow the correct set of rules to be applied, then one could fail to find a valid boundary point. For example, suppose a user clicked after the first space after the question mark in “Are_you_there? _ _ No,_I'm_not”. On a forward iteration searching for a sentence boundary, one would fail to find the boundary before the “N”, because the “?” hadn’t been seen yet.

A second set of rules to determine a “safe” starting point provides a solution. Iterate backward with this second set of rules until a safe starting point is located, then iterate forward from there. Iterate forward to find boundaries that were located between the safe point and the starting point; discard these. The desired boundary is the first one that is not less than the starting point. The safe rules must be designed so that they function correctly no matter what the starting point is, so they have to be conservative in terms of finding boundaries: only finding those boundaries that can be determined by a small context.

This process would represent a significant performance cost if it had to be performed on every search. However, this functionality can be wrapped up in an iterator object, which preserves the information regarding whether it currently is at a valid boundary point. Only if it is reset to an arbitrary location in the text is this extra backup processing performed. The iterator may even cache local values that it has already traversed.

6.5 Tailoring

State machines can also be combined with a code-based or table-based tailoring mechanism. For typical Unicode state machines, a Unicode character is typically passed to a mapping table that maps characters to boundary property values. This mapping can use an efficient mechanism such as a trie. Once a boundary property value is produced, then it is passed to the state machine.

The simplest customization is just to adjust the values coming out of the character mapping table. For example, to mark the appropriate quotation marks for a given language as having the Close sentence boundary property, artificial property values can be introduced for different quotation marks. A table can be applied after the main mapping table to map those artificial character property values to the real ones. To change languages, a different small table is substituted. The only real cost is then an extra array look-up.

For code-based tailoring a different special range of property values can be added. The state machine is set up so that any special property value causes the state machine to halt, and return a particular exception value. When this exception value is detected, the higher-level process can call specialized code according to whatever the exceptional value is. This can all be encapsulated so that it is transparent to the caller.

For example, Thai characters can be mapped to a special property value. When the state machine halts for one of these values, then a Thai word-break engine is invoked internally, to produce boundaries within the subsequent string of Thai characters. These boundaries can then be cached so that subsequent calls for next/previous boundaries merely return the cached values. Similarly Lao characters can be mapped to a different special property value, causing a different engine to be invoked.

7. Testing

There is no requirement by Unicode-conformant implementations to implement these default boundaries. As with the other default specifications, implementations are also free to override (tailor) the results to meet the requirements of different environments or particular languages. For those who do implement the default boundaries as specified above, and wish to check that that their implementation matches that specification, three test files have been made available.

These tests cannot be exhaustive, because of the sheer combinatorics; but they do provide samples that test all pairs of property values, using a representative character for each value, plus certain other sequences.

A sample HTML file is also available that shows various combinations in chart form. The header cells of the chart consist of a property value, followed by a representative code point number. The body cells in the chart show the break status: whether a break occurs between the row property value and the column property value. If the browser supports tool-tips, then hovering the mouse over the code point number will show the character name, general category, line break, and script property values. Hovering over the break status will display the number of the rule responsible for that status.

Note: To determine a boundary it is not sufficient to just test the two adjacent characters, except for the case of the default grapheme clusters.

The chart may be followed by some test cases. These test cases consist of various strings with the break status between each pair of characters shown by blue lines for breaks, and whitespace for non-breaks. Hovering over each character (with tool-tips enabled) shows the character name and property value; hovering over the break status shows the number of the rule responsible for that status.

Acknowledgements

Thanks to Ted Hopp, Andy Heninger, Michael Kaplan, Steve Tolkin, Ken Whistler, and Eric Mader for their feedback on previous versions of this document.

References

Modifications

The following summarizes modifications from the previous versions of this document.

Tracking Number 6.

• Changed Term to be the 4.0.1 UCD property STerm. Note: the new property provides minor corrections as well.

Tracking Number 4.

• Updated boilerplate.
• Use the Grapheme_Extend property. Dropped note on Other_Grapheme_Extend, since those changes are in UCD 4.0.0
• Deleted note on relation to 3.0 text. Replace reference to 3.2 with one to 4.0.
• Replaced the lists of Korean chars by reference to the Hangul_Syllable_Type, with the lists kept as examples. Added reference to the UCD.
• Simplified ALetter and OLetter, since some characters are changing from Sk to Lm, and thus get included; other Sk are not really candidates for words.
• Subtracted characters from certain classes so they wouldn't overlap:
  • CR and LF from Control in Grapheme Break
  • Soft hyphen from MidLetter in Word Break (since it is Cf in 4.0)
  • ATerm, Term and GERESH from Close in Sentence Break
• Added note about finite-state machine; highlighted notes about adjacent characters.
• Fixed the term "interior" (didn't match the rules); and some character names.

Tracking Number 3.

• Removal of two open issues, resolved by UTC
• Changed name of 'character class' to 'property value' for consistency
• Other_Grapheme_Extend now includes characters for canonical closure
• Minor changes to some other property values
• Some additional notes on tailoring words for French, Italian, and Hebrew
• Added 7 Testing
• Minor editing

Tracking Number 2.

• Simplified grapheme cluster.
• Handled format characters appropriately.
• Removed Hiragana × Hiragana from word break, as well as prefix/posfix for numbers (since they should not block whole-word search).
• Modified sentence break to catch edge conditions.
• Added conformance section, with more warnings throughout that these specifications need to be tailored for different languages/orthographic conventions.
• Tightened up the specifications of the character classes.
• Clarified the rule process.
• Added explanations of the interaction with normalization.
• Added an implementation section (incorporating the previous Random Access section).

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