Disclaimer: I have no engineering background whatsoever - please don't hold it against me ;)
What I'm trying to do:
Scan a bunch of text strings and find the ones that
are more than one word
contain title case (at least one capitalized word after the first one)
but exclude specific proper nouns that don't get checked for title case
and disregard any parameters in curly brackets
Example: Today, a Man walked his dogs named {FIDO} and {Fifi} down the Street.
Expectation: Flag the string for title capitalization because of Man and Street, not because of Today, {FIDO} or {Fifi}
Example: Don't post that video on TikTok.
Expectation: No flag because TikTok is a proper noun
I have bits and pieces, none of them error-free from what https://www.regextester.com/ keeps telling me so I'm really hoping for help from this community.
What I've tried (in piece meal but not all together):
(?=([A-Z][a-z]+\s+[A-Z][a-z]+))
^(?!(WordA|WordB)$)
^((?!{*}))
I think your problem is not really solvable solely with regex...
My recommendation would be splitting the input via [\s\W]+ (e.g. with python's re.split, if you really need strings with more than one word, you can check the length of the result), filtering each resulting word if the first character is uppercase (e.g with python's string.isupper) and finally filtering against a dictionary.
[\s\W]+ matches all whitespace and non-word characters, yielding words...
The reasoning behind this different approach: compiling all "proper nouns" in a regex is kinda impossible, using "isupper" also works with non-latin letters (e.g. when your strings are unicode, [A-Z] won't be sufficient to detect uppercase). Filtering utilizing a dictionary is a way more forward approach and much easier to maintain (I would recommend using set or other data type suited for fast lookups.
Maybe if you can define your use case more clearer we can work out a pure regex solution...
Is it possible to use unicode combining characters to for example make the characters x and y appear to be partially overlapping each other?
I know that in layout systems like CSS there are other ways to achieve this, but I specifically want to know if its possible with just unicode so I can for example do it in Slack messages.
No, there is no Unicode mechanism to make arbitrary letters overlap each other. You can put an x above a y using the character U+036F COMBINING LATIN SMALL LETTER X like so: yͯ, but that’s about it.
Latin letters partially overlapping each other serves no semantic function, so it is not part of the Unicode standard. And if it was found to be used to convey actual meaning in some writing system, it would most likely not be encoded as a generalised mechanism but as individual characters representing specific such ligatures.
The Unicode Consortium does not consider styling features like that to be part of plain text. That is also why those bold and italic mathematical letters you sometimes see on Twitter (𝐀, 𝐴, 𝓐 etc.) aren’t implemented as the base letters plus some style modifiers, but as separate character codes entirely. A character that means “display the preceding letter as bold” would have been too general; non-crucial style variation should be dealt with through higher-level protocols (like the CSS you mentioned) which are much more powerful and enjoy more widespread support anyway.
I have a name input field in an app and would like to prevent users from entering emojis. My idea is to filter for any characters from the general categories "Cs" and "So" in the Unicode specification, as this would prevent the bulk of inappropriate characters but allow most characters for writing natural language.
But after reading the spec, I'm not sure if this would preclude, for example, a Pinyin keyboard from submitting Chinese characters that need supplemental code points. (My understanding is still rough.)
Would excluding surrogates still leave most Chinese users with the characters they need to enter their names, or is the original Unicode space not big enough for that to be a reasonable expectation?
Your method would be both ineffective and too excessive.
Not all emoji are outside of the Basic Multilingual Plane (and thus don’t require surrogates in the first place), and not all emoji belong to the general category So. Filtering out only these two groups of characters would leave the following emoji intact:
#️⃣ *️⃣ 0️⃣ 1️⃣ 2️⃣ 3️⃣ 4️⃣ 5️⃣ 6️⃣ 7️⃣ 8️⃣ 9️⃣ ‼️ ⁉️ ℹ️ ↔️ ◼️ ◻️ ◾️ ◽️ ⤴️ ⤵️ 〰️ 〽️
At the same time, this approach would also exclude about 79,000 (and counting) non-emoji characters covering several dozen scripts – many of them historic, but some with active user communities. The majority of all Han (Chinese) characters for instance are encoded outside the BMP. While most of these are of scholarly interest only, you will need to support them regardless especially when you are dealing with personal names. You can never know how uncommon your users’ names might be.
This whole ordeal also hinges on the technical details of your app. Removing surrogates would only work if the framework you are using encodes strings in a format that actually employs surrogates (i.e. UTF-16) and if your framework is simultaneously not aware of how UTF-16 really works (as Java or JavaScript are, for example). Surrogates are never treated as actual characters; they are exceptionally reserved codepoints that exist for the sole purpose of allowing UTF-16 to deal with characters in the higher planes. Other Unicode encodings aren’t even allowed to use them at all.
If your app is written in a language that either uses a different encoding like UTF-8 or is smart enough to process surrogates correctly, then removing Cs characters on input is never going to have any effect because no individual surrogates are ever being exposed to your program. How these characters are entered by the user does not matter because all your app gets to see is the finished product (the actual character codepoints).
If your goal is to remove all emoji and only emoji, then you will have to put a lot of effort into designing your code because the Unicode emoji spec is incredibly convoluted. Most emoji nowadays are constructed out of multiple characters, not all of which are categorised as emoji by themselves. There is no easy way to filter out just emoji from a string other than maintaining an explicit list of every single official emoji which would need to be steadily updated.
Will precluding surrogate code points also impede entering Chinese characters? […] if this would preclude, for example, a Pinyin keyboard from submitting Chinese characters that need supplemental code points.
You cannot intercept how characters are entered, whether via input method editor, copy-paste or dozens of other possibilities. You only get to see a character when it is completed (and an IME's work is done), or depending on the widget toolkit, even only after the text has been submitted. That leaves you with validation. Let's consider a realistic case. From Unihan_Readings.txt 12.0.0 (2018-11-09):
U+20009 ‹𠀉› (the same as U+4E18 丘) a hill; elder; empty; a name
U+22218 ‹𢈘› variant of 鹿 U+9E7F, a deer; surname
U+22489 ‹𢒉› a surname
U+224B9 ‹𢒹› surname
U+25874 ‹𥡴› surname
Assume the user enters 𠀉, then your unnamed – but hopefully Unicode compliant – programming language must consider the text on the grapheme level (1 grapheme cluster) or character level (1 character), not the code unit level (surrogate pair 0xD840 0xDC09). That means that it is okay to exclude characters with the Cs property.
I have a dataset which mixes use of unicode characters \u0421, 'С' and \u0043, 'C'. Is there some sort of unicode comparison which considers those two characters the same? So far I've tried several ICU collations, including the Russian one.
There is no Unicode comparison that treats characters as the same on the basis of visual identity of glyphs. However, Unicode Technical Standard #39, Unicode Security Mechanisms, deals with “confusables” – characters that may be confused with each other due to visual identity or similarity. It includes a data file of confusables as well as “intentionally confusable” pairs, i.e. “characters whose glyphs in any particular typeface would probably be designed to be identical in shape when using a harmonized typeface design”, which mainly consists of pairs of Latin and Cyrillic or Greek letters, like C and С. You would probably need to code your own use of this data, as ICU does not seem to have anything related to the confusable concept.
when you take a look at http://www.unicode.org/Public/UCD/latest/ucd/UnicodeData.txt, you will see that some code positions are annotated for codepoints that are similar in use; however, i'm not aware of any extensive list that covers visual similarities across scripts. you might want to search for URL spoofing using intentional misspellings, which was discussed when they came up with punycode. other than that, your best bet might be to search the data for characters outside the expected using regular expressions, and compile a series of ad-hoc text fixers like text = text.replace /с/, 'c'.
I am using the term "Lexical Encoding" for my lack of a better one.
A Word is arguably the fundamental unit of communication as opposed to a Letter. Unicode tries to assign a numeric value to each Letter of all known Alphabets. What is a Letter to one language, is a Glyph to another. Unicode 5.1 assigns more than 100,000 values to these Glyphs currently. Out of the approximately 180,000 Words being used in Modern English, it is said that with a vocabulary of about 2,000 Words, you should be able to converse in general terms. A "Lexical Encoding" would encode each Word not each Letter, and encapsulate them within a Sentence.
// An simplified example of a "Lexical Encoding"
String sentence = "How are you today?";
int[] sentence = { 93, 22, 14, 330, QUERY };
In this example each Token in the String was encoded as an Integer. The Encoding Scheme here simply assigned an int value based on generalised statistical ranking of word usage, and assigned a constant to the question mark.
Ultimately, a Word has both a Spelling & Meaning though. Any "Lexical Encoding" would preserve the meaning and intent of the Sentence as a whole, and not be language specific. An English sentence would be encoded into "...language-neutral atomic elements of meaning ..." which could then be reconstituted into any language with a structured Syntactic Form and Grammatical Structure.
What are other examples of "Lexical Encoding" techniques?
If you were interested in where the word-usage statistics come from :
http://www.wordcount.org
This question impinges on linguistics more than programming, but for languages which are highly synthetic (having words which are comprised of multiple combined morphemes), it can be a highly complex problem to try to "number" all possible words, as opposed to languages like English which are at least somewhat isolating, or languages like Chinese which are highly analytic.
That is, words may not be easily broken down and counted based on their constituent glyphs in some languages.
This Wikipedia article on Isolating languages may be helpful in explaining the problem.
Their are several major problems with this idea. In most languages, the meaning of a word, and the word associated with a meaning change very swiftly.
No sooner would you have a number assigned to a word, before the meaning of the word would change. For instance, the word "gay" used to only mean "happy" or "merry", but it is now used mostly to mean homosexual. Another example is the morpheme "thank you" which originally came from German "danke" which is just one word. Yet another example is "Good bye" which is a shortening of "God bless you".
Another problem is that even if one takes a snapshot of a word at any point of time, the meaning and usage of the word would be under contention, even within the same province. When dictionaries are being written, it is not uncommon for the academics responsible to argue over a single word.
In short, you wouldn't be able to do it with an existing language. You would have to consider inventing a language of your own, for the purpose, or using a fairly static language that has already been invented, such as Interlingua or Esperanto. However, even these would not be perfect for the purpose of defining static morphemes in an ever-standard lexicon.
Even in Chinese, where there is rough mapping of character to meaning, it still would not work. Many characters change their meanings depending on both context, and which characters either precede or postfix them.
The problem is at its worst when you try and translate between languages. There may be one word in English, that can be used in various cases, but cannot be directly used in another language. An example of this is "free". In Spanish, either "libre" meaning "free" as in speech, or "gratis" meaning "free" as in beer can be used (and using the wrong word in place of "free" would look very funny).
There are other words which are even more difficult to place a meaning on, such as the word beautiful in Korean; when calling a girl beautiful, there would be several candidates for substitution; but when calling food beautiful, unless you mean the food is good looking, there are several other candidates which are completely different.
What it comes down to, is although we only use about 200k words in English, our vocabularies are actually larger in some aspects because we assign many different meanings to the same word. The same problems apply to Esperanto and Interlingua, and every other language meaningful for conversation. Human speech is not a well-defined, well oiled-machine. So, although you could create such a lexicon where each "word" had it's own unique meaning, it would be very difficult, and nigh on impossible for machines using current techniques to translate from any human language into your special standardised lexicon.
This is why machine translation still sucks, and will for a long time to come. If you can do better (and I hope you can) then you should probably consider doing it with some sort of scholarship and/or university/government funding, working towards a PHD; or simply make a heap of money, whatever keeps your ship steaming.
It's easy enough to invent one for yourself. Turn each word into a canonical bytestream (say, lower-case decomposed UCS32), then hash it down to an integer. 32 bits would probably be enough, but if not then 64 bits certainly would.
Before you ding for giving you a snarky answer, consider that the purpose of Unicode is simply to assign each glyph a unique identifier. Not to rank or sort or group them, but just to map each one onto a unique identifier that everyone agrees on.
How would the system handle pluralization of nouns or conjugation of verbs? Would these each have their own "Unicode" value?
As a translations scheme, this is probably not going to work without a lot more work. You'd like to think that you can assign a number to each word, then mechanically translate that to another language. In reality, languages have the problem of multiple words that are spelled the same "the wind blew her hair back" versus "wind your watch".
For transmitting text, where you'd presumably have an alphabet per language, it would work fine, although I wonder what you'd gain there as opposed to using a variable-length dictionary, like ZIP uses.
This is an interesting question, but I suspect you are asking it for the wrong reasons. Are you thinking of this 'lexical' Unicode' as something that would allow you to break down sentences into language-neutral atomic elements of meaning and then be able to reconstitute them in some other concrete language? As a means to achieve a universal translator, perhaps?
Even if you can encode and store, say, an English sentence using a 'lexical unicode', you can not expect to read it and magically render it in, say, Chinese keeping the meaning intact.
Your analogy to Unicode, however, is very useful.
Bear in mind that Unicode, whilst a 'universal' code, does not embody the pronunciation, meaning or usage of the character in question. Each code point refers to a specific glyph in a specific language (or rather the script used by a group of languages). It is elemental at the visual representation level of a glyph (within the bounds of style, formatting and fonts). The Unicode code point for the Latin letter 'A' is just that. It is the Latin letter 'A'. It cannot automagically be rendered as, say, the Arabic letter Alif (ﺍ) or the Indic (Devnagari) letter 'A' (अ).
Keeping to the Unicode analogy, your Lexical Unicode would have code points for each word (word form) in each language. Unicode has ranges of code points for a specific script. Your lexical Unicode would have to a range of codes for each language. Different words in different languages, even if they have the same meaning (synonyms), would have to have different code points. The same word having different meanings, or different pronunciations (homonyms), would have to have different code points.
In Unicode, for some languages (but not all) where the same character has a different shape depending on it's position in the word - e.g. in Hebrew and Arabic, the shape of a glyph changes at the end of the word - then it has a different code point. Likewise in your Lexical Unicode, if a word has a different form depending on its position in the sentence, it may warrant its own code point.
Perhaps the easiest way to come up with code points for the English Language would be to base your system on, say, a particular edition of the Oxford English Dictionary and assign a unique code to each word sequentially. You will have to use a different code for each different meaning of the same word, and you will have to use a different code for different forms - e.g. if the same word can be used as a noun and as a verb, then you will need two codes
Then you will have to do the same for each other language you want to include - using the most authoritative dictionary for that language.
Chances are that this excercise is all more effort than it is worth. If you decide to include all the world's living languages, plus some historic dead ones and some fictional ones - as Unicode does - you will end up with a code space that is so large that your code would have to be extremely wide to accommodate it. You will not gain anything in terms of compression - it is likely that a sentence represented as a String in the original language would take up less space than the same sentence represented as code.
P.S. for those who are saying this is an impossible task because word meanings change, I do not see that as a problem. To use the Unicode analogy, the usage of letters has changed (admittedly not as rapidly as the meaning of words), but it is not of any concern to Unicode that 'th' used to be pronounced like 'y' in the Middle ages. Unicode has a code point for 't', 'h' and 'y' and they each serve their purpose.
P.P.S. Actually, it is of some concern to Unicode that 'oe' is also 'œ' or that 'ss' can be written 'ß' in German
This is an interesting little exercise, but I would urge you to consider it nothing more than an introduction to the concept of the difference in natural language between types and tokens.
A type is a single instance of a word which represents all instances. A token is a single count for each instance of the word. Let me explain this with the following example:
"John went to the bread store. He bought the bread."
Here are some frequency counts for this example, with the counts meaning the number of tokens:
John: 1
went: 1
to: 1
the: 2
store: 1
he: 1
bought: 1
bread: 2
Note that "the" is counted twice--there are two tokens of "the". However, note that while there are ten words, there are only eight of these word-to-frequency pairs. Words being broken down to types and paired with their token count.
Types and tokens are useful in statistical NLP. "Lexical encoding" on the other hand, I would watch out for. This is a segue into much more old-fashioned approaches to NLP, with preprogramming and rationalism abound. I don't even know about any statistical MT that actually assigns a specific "address" to a word. There are too many relationships between words, for one thing, to build any kind of well thought out numerical ontology, and if we're just throwing numbers at words to categorize them, we should be thinking about things like memory management and allocation for speed.
I would suggest checking out NLTK, the Natural Language Toolkit, written in Python, for a more extensive introduction to NLP and its practical uses.
Actually you only need about 600 words for a half decent vocabulary.