Related
Got an interesting one, and can't come up with any solid ideas, so thought maybe someone else may have done something similar.
I want to be able to identify strings of letters in a longer sentence that are not words and remove them. Essentially things like kuashdixbkjshakd
Everything annoyingly is in lowercase which makes it more difficult, but since I only care about English, I'm essentially looking for the opposite of consonant clusters, groups of them that don't make phonetically pronounceable sounds.
Has anyone heard of/done something like this before?
EDIT: this is what ChatGpt tells me
It is difficult to provide a comprehensive list of combinations of consonants that have never appeared in a word in the English language. The English language is a dynamic and evolving language, and new words are being created all the time. Additionally, there are many regional and dialectal variations of the language, which can result in different sets of words being used in different parts of the world.
It is also worth noting that the frequency of use of a particular combination of consonants in the English language is difficult to quantify, as the existing literature on the subject is limited. The best way to determine the frequency of use of a particular combination of consonants would be to analyze a large corpus of written or spoken English.
In general, most combinations of consonants are used in some words in the English language, but some combinations of consonants may be relatively rare. Some examples of relatively rare combinations of consonants in English include "xh", "xw", "ckq", and "cqu". However, it is still possible that some words with these combinations of consonants exist.
You could try to pass every single word inside the sentence to a function that checks wether the word is listed inside a dictionary. There is a good number of dictionary text files on GitHub. To speed up the process: use a hash map :)
You could also use an auto-corretion API or a library.
Algorithm to combine both methods:
Run sentence through auto correction
Run every word through dictionary
Delete words that aren't listed in the dictionary
This could remove typos and words that are non-existent.
You could train a simple model on sequences of characters which are permitted in the language(s) you want to support, and then flag any which contain sequences which are not in the training data.
The LangId language detector in SpamAssassin implements the Cavnar & Trenkle language-identification algorithm which basically uses a sliding window over the text and examines the adjacent 1 to 5 characters at each position. So from the training data "abracadabra" you would get
a 5
ab 2
abr 2
abra 2
abrac 1
b 2
br 2
bra 2
brac 1
braca 1
:
With enough data, you could build a model which identifies unusual patterns (my suggestion would be to try a window size of 3 or smaller for a start, and train it on several human languages from, say, Wikipedia) but it's hard to predict how precise exactly this will be.
SpamAssassin is written in Perl and it should not be hard to extract the language identification module.
As an alternative, there is a library called libtextcat which you can run standalone from C code if you like. The language identification in LibreOffice uses a fork which they adapted to use Unicode specifically, I believe (though it's been a while since I last looked at that).
Following Cavnar & Trenkle, all of these truncate the collected data to a few hundred patterns; you would probably want to extend this to cover up to all the 3-grams you find in your training data at least.
Perhaps see also Gertjan van Noord's link collection: https://www.let.rug.nl/vannoord/TextCat/
Depending on your test data, you could still get false positives e.g. on peculiar Internet domain names and long abbreviations. Tweak the limits for what you want to flag - I would think that GmbH should be okay even if you didn't train on German, but something like 7 or more letters long should probably be flagged and manually inspected.
This will match words with more than 5 consonants (you probably want "y" to not be considered a consonant, but it's up to you):
\b[a-z]*[b-z&&[^aeiouy]]{6}[a-z]*\b
See live demo.
5 was chosen because I believe witchcraft has the longest chain of consonants of any English word. You could dial back "6" in the regex to say 5 or even 4 if you don't mind matching some outliers.
It seems hard to detect a sentence boundary in a text. Quotation marks like .!? may be used to delimite sentences but not so accurate as there may be ambiguous words and quotations such as U.S.A or Prof. or Dr. I am studying Tperlregex library and Regular Expression Cookbook by Jan Goyvaerts but I do not know how to write the expression that detects sentence?
What may be comparatively accurate expression using Tperlregex in delphi?
Thanks
First, you probably need to arrive at your own definition of what a "sentence" is, then implement that definition. For example, how about:
He said: "It's OK!"
Is it one sentence or two? A general answer is irrelevant. Decide whether you want it to interpret it as one or two sentences, and proceed accordingly.
Second, I don't think I'd be using regular expressions for this. Instead, I would scan each character and try to detect sequences. A period by itself may not be enough to delimit a sentence, but a period followed by whitespace or carriage return (or end of string) probably does. This immediately lets you weed out U.S.A (periods not followed by whitespace).
For common abbreviations like Prof. an Dr. it may be a good idea to create a dictionary - perhaps editable by your users, since each language will have its own set of common abbreviations.
Each language will have its own set of punctuation rules too, which may affect how you interpret punctuation characters. For example, English tends to put a period inside the parentheses (like this.) while Polish does the opposite (like this). The same difference will apply to double quotes, single quotes (some languages don't use them at all, sometimes they are indistinguishable from apostrophes etc.). Your rules may well have to be language-specific, at least in part.
In the end, you may approximate the human way of delimiting sentences, but there will always be cases that can throw the analysis off. For example, assuming that you have a dictionary that recognizes "Prof." as an abbreviation, what are you going to do about
Most people called him Professor Jones, but to me he was simply The Prof.
Even if you have another sentence that follows and starts with a capital letter, that still won't help you know where the sentence ends, because it might as well be
Most people called him Professor Jones, but to me he was simply Prof. Bill.
Check my tutorial here http://code.google.com/p/graph-expression/wiki/SentenceSplitting. This concrete example can be easily rewritten to regular expressions and some imperative code.
It will be wise to use a NLP processor with a pre-trained model. EnglishSD.nbin is one such model that is available for OpenNLP and it can be used in Visual Studio with SharpNLP.
The advantage of using this method is numerous. For example consider the input
Prof. Jessica is a wonderful woman. She is a native of U.S.A. She is married to Mr. Jacob Jr.
If you are using a regex split, for example
string[] sentences = Regex.Split(text, #"(?<=['""A-Za-z0-9][\.\!\?])\s+(?=[A-Z])");
Then the above input will be split as
Prof.
Jessica is a wonderful woman.
She is a native of U.
S.
A.
She is married to Mr.
Jacob Jr.
However the desired output is
Prof. Jessica is a wonderful woman.
She is a native of U.S.A. She is married to Mr. Jacob Jr.
This kind of logical sentence split can be achieved only using trained models from OpenNLP project. The method is as simple as this.
private string mModelPath = #"C:\Users\ATS\Documents\Visual Studio 2012\Projects\Google_page_speed_json\Google_page_speed_json\bin\Release\";
private OpenNLP.Tools.SentenceDetect.MaximumEntropySentenceDetector mSentenceDetector;
private string[] SplitSentences(string paragraph)
{
if (mSentenceDetector == null)
{
mSentenceDetector = new OpenNLP.Tools.SentenceDetect.EnglishMaximumEntropySentenceDetector(mModelPath + "EnglishSD.nbin");
}
return mSentenceDetector.SentenceDetect(paragraph);
}
where mModelPath is the path of the directory containing the nbin file.
The mSentenceDetector is derived from the OpenNLP dll.
You can get the desired output by
string[] sentences = SplitSentences(text);
Kindly read through this article I have written for integrating SharpNLP with your Application in Visual Studio to make use of the NLP tools
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
I'm trying to search for the word Gadaffi, which can be spelled in many different ways. What's the best regular expression to search for this?
This is a list of 30 variants:
Gadaffi
Gadafi
Gadafy
Gaddafi
Gaddafy
Gaddhafi
Gadhafi
Gathafi
Ghadaffi
Ghadafi
Ghaddafi
Ghaddafy
Gheddafi
Kadaffi
Kadafi
Kaddafi
Kadhafi
Kazzafi
Khadaffy
Khadafy
Khaddafi
Qadafi
Qaddafi
Qadhafi
Qadhdhafi
Qadthafi
Qathafi
Quathafi
Qudhafi
Kad'afi
My best attempt so far is:
\b[KG]h?add?af?fi$\b
But I still seem to be missing some variants. Any suggestions?
Easy... (Qadaffi|Khadafy|Qadafi|...)... it's self-documented, maintainable, and assuming your regexp engine actually compiles regular expressions (rather than interpreting them), it will compile to the same DFA that a more obfuscated solution would.
Writing compact regular expressions is like using short variable names to speed up a program. It only helps if your compiler is brain-dead.
\b[KGQ]h?add?h?af?fi\b
Arabic transcription is (Wiki says) "Qaḏḏāfī", so maybe adding a Q. And one H ("Gadhafi", as the article (see below) mentions).
Btw, why is there a $ at the end of the regex?
Btw, nice article on the topic:
Gaddafi, Kadafi, or Qaddafi? Why is the Libyan leader’s name spelled so many different ways?.
EDIT
To match all the names in the article you've mentioned later, this should match them all. Let's just hope it won't match a lot of other stuff :D
\b(Kh?|Gh?|Qu?)[aeu](d['dt]?|t|zz|dhd)h?aff?[iy]\b
One interesting thing to note from your list of potential spellings is that there's only 3 Soundex values for the contained list (if you ignore the outlier 'Kazzafi')
G310, K310, Q310
Now, there are false positives in there ('Godby' also is G310), but by combining the limited metaphone hits as well, you can eliminate them.
<?
$soundexMatch = array('G310','K310','Q310');
$metaphoneMatch = array('KTF','KTHF','FTF','KHTF','K0F');
$text = "This is a big glob of text about Mr. Gaddafi. Even using compound-Khadafy terms in here, then we might find Mr Qudhafi to be matched fairly well. For example even with apostrophes sprinkled randomly like in Kad'afi, you won't find false positives matched like godfrey, or godby, or even kabbadi";
$wordArray = preg_split('/[\s,.;-]+/',$text);
foreach ($wordArray as $item){
$rate = in_array(soundex($item),$soundexMatch) + in_array(metaphone($item),$metaphoneMatch);
if ($rate > 1){
$matches[] = $item;
}
}
$pattern = implode("|",$matches);
$text = preg_replace("/($pattern)/","<b>$1</b>",$text);
echo $text;
?>
A few tweaks, and lets say some cyrillic transliteration, and you'll have a fairly robust solution.
Using CPAN module Regexp::Assemble:
#!/usr/bin/env perl
use Regexp::Assemble;
my $ra = Regexp::Assemble->new;
$ra->add($_) for qw(Gadaffi Gadafi Gadafy Gaddafi Gaddafy
Gaddhafi Gadhafi Gathafi Ghadaffi Ghadafi
Ghaddafi Ghaddafy Gheddafi Kadaffi Kadafi
Kaddafi Kadhafi Kazzafi Khadaffy Khadafy
Khaddafi Qadafi Qaddafi Qadhafi Qadhdhafi
Qadthafi Qathafi Quathafi Qudhafi Kad'afi);
say $ra->re;
This produces the following regular expression:
(?-xism:(?:G(?:a(?:d(?:d(?:af[iy]|hafi)|af(?:f?i|y)|hafi)|thafi)|h(?:ad(?:daf[iy]|af?fi)|eddafi))|K(?:a(?:d(?:['dh]a|af?)|zza)fi|had(?:af?fy|dafi))|Q(?:a(?:d(?:(?:(?:hd)?|t)h|d)?|th)|u(?:at|d)h)afi))
I think you're over complicating things here. The correct regex is as simple as:
\u0627\u0644\u0642\u0630\u0627\u0641\u064a
It matches the concatenation of the seven Arabic Unicode code points that forms the word القذافي (i.e. Gadaffi).
If you want to avoid matching things that no-one has used (ie avoid tending towards ".+") your best approach would be to create a regular expression that's just all the alternatives (eg. (Qadafi|Kadafi|...)) then compile that to a DFA, and then convert the DFA back into a regular expression. Assuming a moderately sensible implementation that would give you a "compressed" regular expression that's guaranteed not to contain unexpected variants.
If you've got a concrete listing of all 30 possibilities, just concatenate them all together with a bunch of "ors". Then you can be sure that it only matches the exact things you've listed, and no more. Your RE engine will probably be able to optimize in further, and, well, with 30 choices even if it doesn't it's still not a big deal. Trying to fiddle around with manually turning it into a "clever" RE can't possibly turn out better and may turn out worse.
(G|Gh|K|Kh|Q|Qh|Q|Qu)(a|au|e|u)(dh|zz|th|d|dd)(dh|th|a|ha|)(\x27|)(a|)(ff|f)(i|y)
Certainly not the most optimized version, split on syllables to maximize matches while trying to make sure we don't get false positives.
Well since you are matching small words why don't you try a similarity search engine with the Levenshtein distance? You can allow at most k insertions or deletions. This way you can change the distance function to other things that work better for your specific problem. There are many functions available in the simMetrics library.
A possible alternative is the online tool for generate regular expressions from examples http://regex.inginf.units.it.
Give it a chance!
Why not do a mixed approach? Something between a list of all possibilities and a complicated Regex that matches far too much.
Regex is about pattern matching and I can't see a pattern for all variants in the list. Trying to do so, will also find things like "Gazzafy" or "Quud'haffi" which are most probably not a used variant and definitly not on the list.
But I can see patterns for some of the variants, and so I ended up with this:
\b(?:Gheddafi|Gathafi|Kazzafi|Kad'afi|Qadhdhafi|Qadthafi|Qudhafi|Qu?athafi|[KG]h?add?h?aff?[iy]|Qad[dh]?afi)\b
At the beginning I list the ones where I can't see a pattern, then followed by some variants where there are patterns.
See it here on www.rubular.com
I know this is an old question, but...
Neither of these two regexes is the prettiest, but they are optimized and both match ALL the variations in the original post.
"Little Beauty" #1
(?:G(?:a(?:d(?:d(?:af[iy]|hafi)|af(?:f?i|y)|hafi)|thafi)|h(?:ad(?:daf[iy]|af?fi)|eddafi))|K(?:a(?:d(?:['dh]a|af?)|zza)fi|had(?:af?fy|dafi))|Q(?:a(?:d(?:(?:(?:hd)?|t)h|d)?|th)|u(?:at|d)h)afi)
"Little Beauty" #2
(?:(?:Gh|[GK])adaff|(?:(?:Gh|[GKQ])ad|(?:Ghe|(?:[GK]h|[GKQ])a)dd|(?:Gadd|(?:[GKQ]a|Q(?:adh|u))d|(?:Qad|(?:Qu|[GQ])a)t)h|Ka(?:zz|d'))af)i|(?:Khadaff|(?:(?:Kh|G)ad|Gh?add)af)y
Rest in Peace, Muammar.
Just an addendum: you should add "Gheddafi" as alternate spelling. So the RE should be
\b[KG]h?[ae]dd?af?fi$\b
[GQK][ahu]+[dtez]+\'?[adhz]+f{1,2}(i|y)
In parts:
[GQK]
[ahu]+
[dtez]+
\'?
[adhz]+
f{1,2}(i|y)
Note: Just wanted to give a shot at this.
What else starts with Q, G, or K, has a d, z or t in the middle, and ends in "fi" the people actually search for?
/\b[GQK].+[dzt].+fi\b/i
Done.
>>> print re.search(a, "Gadasadasfiasdas") != None
False
>>> print re.search(a, "Gadasadasfi") != None
True
>>> print re.search(a, "Qa'dafi") != None
True
Interesting that I'm getting downvoted. Can someone leave some false positives in the comments?
Closed. This question is opinion-based. It is not currently accepting answers.
Want to improve this question? Update the question so it can be answered with facts and citations by editing this post.
Closed 9 years ago.
Improve this question
I'm thinking of presenting questions in the form of "here is your input: [foo], here are the capture groups/results: [bar]" (and maybe writing a small script to test their answers for my results).
What are some good regex questions to ask? I need everything from beginner questions like "validate a 4 digit number" to "extract postal codes from addresses".
A few that I can think off the top of my head:
Phone numbers in any format e.g. 555-5555, 555 55 55 55, (555) 555-555 etc.
Remove all html tags from text.
Match social security number (Finnish one is easy;)
All IP addresses
IP addresses with shorthand netmask (xx.xx.xx.xx/yy)
There's a bunch of examples of various regular expression techniques over at www.regular-expressions.info - everything for simple literal matching to backreferences and lookahead.
To keep things a bit more interesting than the usual email/phone/url stuff, try looking for more original exercises. Avoid boredom.
For example, have a look at the Forsysth-Edwards Notation which is used for describing a particular board position of a chess game.
Have your students validate and extract all the bits of information from a string like this:
rnbqkbnr/pp1ppppp/8/2p5/4P3/5N2/PPPP1PPP/RNBQKB1R b KQkq - 1 2
Additionaly, have a look at algebraic chess notation, used to describe moves. Extract chess moves out of a piece of text (and make them bold).
1. e4 e5 2. Nf3 Black now defends his pawn 2...Nc6 3. Bb5 Black threatens c4
Validate phone numbers (extract area code + rest of number with grouping) (Assuming US phone number, otherwise generalize for you style)
Play around with validating email address (probably want to tell the students that this is hugely complicated regular expression but for simple ones it is pretty straight forward)
regexplib.com has a good library you can search through for examples.
H0w about extract first name, middle name, last name, personal suffix (Jr., III, etc.) from a format like:
Smith III, John Paul
How about Reg Ex to remove line breaks and tabs from the input
I would start with the common ones:
validate email
validate phone number
separate the parts of a URL
Be cruel. Tell them parse HTML.
RegEx match open tags except XHTML self-contained tags
Are you teaching them theory of finite automata as well?
Here is a good one: parse the addresses of churches correctly from this badly structured format (copy and paste it as text first)
http://www.churchangel.com/WEBNY/newhart.htm
I'm a fan of parsing date strings. Define a few common data formats, as well as time and date-time formats. These are often good exercises because some dates are simple mixes of digits and punctuation. There's a limited degree of freedom in parsing dates.
Just to throw them for a loop, why not reword a question or two to suggest that they write a regular expression to generate data fitting a specific pattern like email addresses, phone numbers, etc.? It's the same thing as validating, but can help them get out of the mindset that regex is just for validation (whereas the data generation tool in visual studio uses regex to randomly generate data).
Rather than teaching examples based from the data set, I would do examples from the perspective of the rule set to get basics across. Give them simple examples to solve that leads them to use ONE of several basic groupings in each solution. Then have a couple of "compound" regex's at the end.
Simple:
s/abc/def/
Spinners and special characters:
s/a\s*b/abc/
Grouping:
s/[abc]/def/
Backreference:
s/ab(c)/def$1/
Anchors:
s/^fred/wilma/
s/$rubble/and betty/
Modifiers:
s/Abcd/def/gi
After this, I would give a few examples illustrating the pitfalls of trying to match html tags or other strings that shouldn't be done with regex's to show the limitations.
Try to think of some tests that don't include ones that can be found with Google.
Asking a email validator should pose no trouble finding..
Try something like a 5 proof test.
Input 5 digit. Sum up each digit must be dividable by five: 12345 = 1+2+3+4+5 = 15 / 5 = 3(.0)
Although this seems like a trivial question, I am quite sure it is not :)
I need to validate names and surnames of people from all over the world. Imagine a huge list of miilions of names and surnames where I need to remove as well as possible any cruft I identify. How can I do that with a regular expression? If it were only English ones I think that this would cut it:
^[a-z -']+$
However, I need to support also these cases:
other punctuation symbols as they might be used in different countries (no idea which, but maybe you do!)
different Unicode letter sets (accented letter, greek, japanese, chinese, and so on)
no numbers or symbols or unnecessary punctuation or runes, etc..
titles, middle initials, suffixes are not part of this data
names are already separated by surnames.
we are prepared to force ultra rare names to be simplified (there's a person named '#' in existence, but it doesn't make sense to allow that character everywhere. Use pragmatism and good sense.)
note that many countries have laws about names so there are standards to follow
Is there a standard way of validating these fields I can implement to make sure that our website users have a great experience and can actually use their name when registering in the list?
I would be looking for something similar to the many "email address" regexes that you can find on google.
I sympathize with the need to constrain input in this situation, but I don't believe it is possible - Unicode is vast, expanding, and so is the subset used in names throughout the world.
Unlike email, there's no universally agreed-upon standard for the names people may use, or even which representations they may register as official with their respective governments. I suspect that any regex will eventually fail to pass a name considered valid by someone, somewhere in the world.
Of course, you do need to sanitize or escape input, to avoid the Little Bobby Tables problem. And there may be other constraints on which input you allow as well, such as the underlying systems used to store, render or manipulate names. As such, I recommend that you determine first the restrictions necessitated by the system your validation belongs to, and create a validation expression based on those alone. This may still cause inconvenience in some scenarios, but they should be rare.
I'll try to give a proper answer myself:
The only punctuations that should be allowed in a name are full stop, apostrophe and hyphen. I haven't seen any other case in the list of corner cases.
Regarding numbers, there's only one case with an 8. I think I can safely disallow that.
Regarding letters, any letter is valid.
I also want to include space.
This would sum up to this regex:
^[\p{L} \.'\-]+$
This presents one problem, i.e. the apostrophe can be used as an attack vector. It should be encoded.
So the validation code should be something like this (untested):
var name = nameParam.Trim();
if (!Regex.IsMatch(name, "^[\p{L} \.\-]+$"))
throw new ArgumentException("nameParam");
name = name.Replace("'", "'"); //' does not work in IE
Can anyone think of a reason why a name should not pass this test or a XSS or SQL Injection that could pass?
complete tested solution
using System;
using System.Text.RegularExpressions;
namespace test
{
class MainClass
{
public static void Main(string[] args)
{
var names = new string[]{"Hello World",
"John",
"João",
"タロウ",
"やまだ",
"山田",
"先生",
"мыхаыл",
"Θεοκλεια",
"आकाङ्क्षा",
"علاء الدين",
"אַבְרָהָם",
"മലയാളം",
"상",
"D'Addario",
"John-Doe",
"P.A.M.",
"' --",
"<xss>",
"\""
};
foreach (var nameParam in names)
{
Console.Write(nameParam+" ");
var name = nameParam.Trim();
if (!Regex.IsMatch(name, #"^[\p{L}\p{M}' \.\-]+$"))
{
Console.WriteLine("fail");
continue;
}
name = name.Replace("'", "'");
Console.WriteLine(name);
}
}
}
}
I would just allow everything (except an empty string) and assume the user knows what his name is.
There are 2 common cases:
You care that the name is accurate and are validating against a real paper passport or other identity document, or against a credit card.
You don't care that much and the user will be able to register as "Fred Smith" (or "Jane Doe") anyway.
In case (1), you can allow all characters because you're checking against a paper document.
In case (2), you may as well allow all characters because "123 456" is really no worse a pseudonym than "Abc Def".
I would think you would be better off excluding the characters you don't want with a regex. Trying to get every umlaut, accented e, hyphen, etc. will be pretty insane. Just exclude digits (but then what about a guy named "George Forman the 4th") and symbols you know you don't want like ##$%^ or what have you. But even then, using a regex will only guarantee that the input matches the regex, it will not tell you that it is a valid name.
EDIT after clarifying that this is trying to prevent XSS: A regex on a name field is obviously not going to stop XSS on its own. However, this article has a section on filtering that is a starting point if you want to go that route:
s/[\<\>\"\'\%\;\(\)\&\+]//g;
"Secure Programming for Linux and Unix HOWTO" by David A. Wheeler, v3.010 Edition (2003)
v3.72, 2015-09-19 is a more recent version.
BTW, do you plan to only permit the Latin alphabet, or do you also plan to try to validate Chinese, Arabic, Hindi, etc.?
As others have said, don't even try to do this. Step back and ask yourself what you are actually trying to accomplish. Then try to accomplish it without making any assumptions about what people's names are, or what they mean.
I don’t think that’s a good idea. Even if you find an appropriate regular expression (maybe using Unicode character properties), this wouldn’t prevent users from entering pseudo-names like John Doe, Max Mustermann (there even is a person with that name), Abcde Fghijk or Ababa Bebebe.
You could use the following regex code to validate 2 names separeted by a space with the following regex code:
^[A-Za-zÀ-ú]+ [A-Za-zÀ-ú]+$
or just use:
[[:lower:]] = [a-zà-ú]
[[:upper:]] =[A-ZÀ-Ú]
[[:alpha:]] = [A-Za-zÀ-ú]
[[:alnum:]] = [A-Za-zÀ-ú0-9]
It's a very difficult problem to validate something like a name due to all the corner cases possible.
Corner Cases
Anything anything here
Sanitize the inputs and let them enter whatever they want for a name, because deciding what is a valid name and what is not is probably way outside the scope of whatever you're doing; given the range of potential strange - and legal names is nearly infinite.
If they want to call themselves Tricyclopltz^2-Glockenschpiel, that's their problem, not yours.
A very contentious subject that I seem to have stumbled along here. However sometimes it's nice to head dear little-bobby tables off at the pass and send little Robert to the headmasters office along with his semi-colons and SQL comment lines --.
This REGEX in VB.NET includes regular alphabetic characters and various circumflexed european characters. However poor old James Mc'Tristan-Smythe the 3rd will have to input his pedigree in as the Jim the Third.
<asp:RegularExpressionValidator ID="RegExValid1" Runat="server"
ErrorMessage="ERROR: Please enter a valid surname<br/>" SetFocusOnError="true" Display="Dynamic"
ControlToValidate="txtSurname" ValidationGroup="MandatoryContent"
ValidationExpression="^[A-Za-z'\-\p{L}\p{Zs}\p{Lu}\p{Ll}\']+$">
This one worked perfectly for me in JavaScript:
^[a-zA-Z]+[\s|-]?[a-zA-Z]+[\s|-]?[a-zA-Z]+$
Here is the method:
function isValidName(name) {
var found = name.search(/^[a-zA-Z]+[\s|-]?[a-zA-Z]+[\s|-]?[a-zA-Z]+$/);
return found > -1;
}
Steps:
first remove all accents
apply the regular expression
To strip the accents:
private static string RemoveAccents(string s)
{
s = s.Normalize(NormalizationForm.FormD);
StringBuilder sb = new StringBuilder();
for (int i = 0; i < s.Length; i++)
{
if (CharUnicodeInfo.GetUnicodeCategory(s[i]) != UnicodeCategory.NonSpacingMark) sb.Append(s[i]);
}
return sb.ToString();
}
This somewhat helps:
^[a-zA-Z]'?([a-zA-Z]|\.| |-)+$
This one should work
^([A-Z]{1}+[a-z\-\.\']*+[\s]?)*
Add some special characters if you need them.