I have various instance of strings such as:
- hello world,i am 2000to -> hello world, i am 2000 to
- the state was 56,869,12th -> the state was 66,869, 12th
- covering.2% -> covering. 2%
- fiji,295,000 -> fiji, 295,000
For dealing with first case, I came up with two step regex:
re.sub(r"(?<=[,])(?=[^\s])(?=[^0-9])", r" ", text) # hello world, i am 20,000to
re.sub(r"(?<=[0-9])(?=[.^[a-z])", r" ", text) # hello world, i am 20,000 to
But this breaks the text in some different ways and other cases are not covered as well. Can anyone suggest a more general regex that solves all cases properly. I've tried using replace, but it does some unintended replacements which in turn raise some other problems. I'm not an expert in regex, would appreciate pointers.
This approach covers your cases above by breaking the text into tokens:
in_list = [
'hello world,i am 2000to',
'the state was 56,869,12th',
'covering.2%',
'fiji,295,000',
'and another example with a decimal 12.3not4,5 is right out',
'parrot,, is100.00% dead'
'Holy grail runs for this portion of 100 minutes,!, 91%. Fascinating'
]
tokenizer = re.compile(r'[a-zA-Z]+[\.,]?|(?:\d{1,3}(?:,\d{3})+|\d+)(?:\.\d+)?(?:%|st|nd|rd|th)?[\.,]?')
for s in in_list:
print(' '.join(re.findall(pattern=tokenizer, string=s)))
# hello world, i am 2000 to
# the state was 56,869, 12th
# covering. 2%
# fiji, 295,000
# and another example with a decimal 12.3 not 4, 5 is right out
# parrot, is 100.00% dead
# Holy grail runs for this portion of 100 minutes, 91%. Fascinating
Breaking up the regex, each token is the longest available substring with:
Only letters with or without a period or comma,[a-zA-Z]+[\.,]?
OR |
A number-ish expression which could be
1 to 3 digits \d{1,3} followed by any number of groups of comma + 3 digits (?:,\d{3})+
OR | any number of comma-free digits \d+
optionally a decimal place followed by at least one digit (?:\.\d+),
optionally a suffix (percent, 'st', 'nd', 'rd', 'th') (?:[\.,%]|st|nd|rd|th)?
optionally period or comma [\.]?
Note the (?:blah) is used to suppress re.findall's natural desire to tell you how every parenthesized group matches up on an individual basis. In this case we just want it to walk forward through the string, and the ?: accomplishes this.
I am trying to do autodetection of bra size in a list of clothes. While I managed to extract only the bra items, I am now looking at extracting the size information and I think I am almost there (thanks to the stackoverflow community). However, there is a particular case that I could not find on another post.
I am using:
regexp = re.compile(r' \d{2,3} ?[a-fA-F]([^bce-zBCE-Z]|$)')
So
Possible white space if not at the beginning of the description
two or three numbers
Another possible white space or not
Any letters (lower or upper case) between A and F
and then another letter for the two special case AA and FF or the end of the string.
My question is, is there a way to have the second letter to be a match of the first letter (AA or FF) because in my case, my code output me some BA and CA size which are not existing
Examples:
Not working:
"bh sexig top matchande h&m genomskinligt parti svart detaljer 42 basic plain" return "42 ba" instead of not found
"puma, sport-bh, strl: 34cd, svart/grå", I guess the customer meant c/d
Working fine:
"victoria's secret, bh, strl: 32c, gul/vit" returns "32 c"
"pink victorias secret bh 75dd burgundy" returns "75 dd"
Thanks!
You might use
\d{2,3} ?([a-fA-F])\1?(?![a-fA-F])
Explanation
\d{2,3} ? Match a space, 2-3 digits and optional space
([a-fA-F])\1? Capture a-fA-F in group 1 followed by an optional backreference to group 1
(?![a-fA-F]) Negative lookahead, assert what is on the right is not a-fA-F
Regex demo
I need to retrieve the definition of an acronym based on the number of letters enclosed in parentheses. For the data I'm dealing with, the number of letters in parentheses corresponds to the number of words to retrieve. I know this isn't a reliable method for getting abbreviations, but in my case it will be. For example:
String = 'Although family health history (FHH) is commonly accepted as an important risk factor for common, chronic diseases, it is rarely considered by a nurse practitioner (NP).'
Desired output: family health history (FHH), nurse practitioner (NP)
I know how to extract parentheses from a string, but after that I am stuck. Any help is appreciated.
import re
a = 'Although family health history (FHH) is commonly accepted as an
important risk factor for common, chronic diseases, it is rarely considered
by a nurse practitioner (NP).'
x2 = re.findall('(\(.*?\))', a)
for x in x2:
length = len(x)
print(x, length)
Use the regex match to find the position of the start of the match. Then use python string indexing to get the substring leading up to the start of the match. Split the substring by words, and get the last n words. Where n is the length of the abbreviation.
import re
s = 'Although family health history (FHH) is commonly accepted as an important risk factor for common, chronic diseases, it is rarely considered by a nurse practitioner (NP).'
for match in re.finditer(r"\((.*?)\)", s):
start_index = match.start()
abbr = match.group(1)
size = len(abbr)
words = s[:start_index].split()[-size:]
definition = " ".join(words)
print(abbr, definition)
This prints:
FHH family health history
NP nurse practitioner
does this solve your problem?
a = 'Although family health history (FHH) is commonly accepted as an important risk factor for common, chronic diseases, it is rarely considered by a nurse practitioner (NP).'
splitstr=a.replace('.','').split(' ')
output=''
for i,word in enumerate(splitstr):
if '(' in word:
w=word.replace('(','').replace(')','').replace('.','')
for n in range(len(w)+1):
output=splitstr[i-n]+' '+output
print(output)
actually, Keatinge beat me to it
An idea, to use a recursive pattern with PyPI regex module.
\b[A-Za-z]+\s+(?R)?\(?[A-Z](?=[A-Z]*\))\)?
See this pcre demo at regex101
\b[A-Za-z]+\s+ matches a word boundary, one or more alpha, one or more white space
(?R)? recursive part: optionally paste the pattern from start
\(? need to make the parenthesis optional for recursion to fit in \)?
[A-Z](?=[A-Z]*\) match one upper alpha if followed by closing ) with any A-Z in between
Does not check if the first word letter actually match the letter at position in the abbreviation.
Does not check for an opening parenthesis in front of the abbreviation. To check, add a variable length lookbehind. Change [A-Z](?=[A-Z]*\)) to (?<=\([A-Z]*)[A-Z](?=[A-Z]*\)).
Using re with list-comprehension
x_lst = [ str(len(i[1:-1])) for i in re.findall('(\(.*?\))', a) ]
[re.search( r'(\S+\s+){' + i + '}\(.{' + i + '}\)', a).group(0) for i in x_lst]
#['family health history (FHH)', 'nurse practitioner (NP)']
This solution isn't particularly clever, it simpy searches for the acronyms and then builds up a pattern to extract the words ahead of each one:
import re
string = "Although family health history (FHH) is commonly accepted as an important risk factor for common, chronic diseases, it is rarely considered by a nurse practitioner (NP)."
definitions = []
for acronym in re.findall(r'\(([A-Z]+?)\)', string):
length = len(acronym)
match = re.search(r'(?:\w+\W+){' + str(length) + r'}\(' + acronym + r'\)', string)
definitions.append(match.group(0))
print(", ".join(definitions))
OUTPUT
> python3 test.py
family health history (FHH), nurse practitioner (NP)
>
I am trying to split a sentence correctly bases on normal grammatical rules in python.
The sentence I want to split is
s = """Mr. Smith bought cheapsite.com for 1.5 million dollars,
i.e. he paid a lot for it. Did he mind? Adam Jones Jr. thinks he didn't. In any case, this isn't true... Well, with a
probability of .9 it isn't."""
The expected output is
Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it.
Did he mind?
Adam Jones Jr. thinks he didn't.
In any case, this isn't true...
Well, with a probability of .9 it isn't.
To achieve this I am using regular , after a lot of searching I came upon the following regex which does the trick.The new_str was jut to remove some \n from 's'
m = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s',new_str)
for i in m:
print (i)
Mr. Smith bought cheapsite.com for 1.5 million dollars,i.e. he paid a lot for it.
Did he mind?
Adam Jones Jr. thinks he didn't.
In any case, this isn't true...
Well, with aprobability of .9 it isn't.
So the way I understand the reg ex is that we are first selecting
1) All the characters like i.e
2) From the filtered spaces from the first selection ,we select those characters
which dont have words like Mr. Mrs. etc
3) From the filtered 2nd step we select only those subjects where we have either dot or question and are preceded by a space.
So I tried to change the order as below
1) Filter out all the titles first.
2) From the filtered step select those that are preceded by space
3) remove all phrases like i.e
but when I do that the blank after is also split
m = re.split(r'(?<![A-Z][a-z]\.)(?<=\.|\?)\s(?<!\w\.\w.)',new_str)
for i in m:
print (i)
Mr. Smith bought cheapsite.com for 1.5 million dollars,i.e.
he paid a lot for it.
Did he mind?
Adam Jones Jr. thinks he didn't.
In any case, this isn't true...
Well, with aprobability of .9 it isn't.
Shouldn't the last step in the modified procedure be capable in identifying phrases like i.e ,why is it failing to detect it ?
First, the last . in (?<!\w\.\w.) looks suspicious, if you need to match a literal dot with it, escape it ((?<!\w\.\w\.)).
Coming back to the question, when you use r'(?<![A-Z][a-z]\.)(?<=\.|\?)\s(?<!\w\.\w.)' regex, the last negative lookbehind checks if the position after a whitespace is not preceded with a word char, dot, word char, any char (since the . is unescaped). This condition is true, because there are a dot, e, another . and a space before that position.
To make the lookbehind work that same way as when it was before \s, put the \s into the lookbehind pattern, too:
(?<![A-Z][a-z]\.)(?<=\.|\?)\s(?<!\w\.\w.\s)
See the regex demo
Another enhancement can be using a character class in the second lookbehind: (?<=\.|\?) -> (?<=[.?]).
This question already has answers here:
How can I split a text into sentences?
(20 answers)
Closed 3 years ago.
I want to make a list of sentences from a string and then print them out. I don't want to use NLTK to do this. So it needs to split on a period at the end of the sentence and not at decimals or abbreviations or title of a name or if the sentence has a .com This is attempt at regex that doesn't work.
import re
text = """\
Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it. Did he mind? Adam Jones Jr. thinks he didn't. In any case, this isn't true... Well, with a probability of .9 it isn't.
"""
sentences = re.split(r' *[\.\?!][\'"\)\]]* *', text)
for stuff in sentences:
print(stuff)
Example output of what it should look like
Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it.
Did he mind?
Adam Jones Jr. thinks he didn't.
In any case, this isn't true...
Well, with a probability of .9 it isn't.
(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s
Try this. split your string this.You can also check demo.
http://regex101.com/r/nG1gU7/27
Ok so sentence-tokenizers are something I looked at in a little detail, using regexes, nltk, CoreNLP, spaCy. You end up writing your own and it depends on the application. This stuff is tricky and valuable and people don't just give their tokenizer code away. (Ultimately, tokenization is not a deterministic procedure, it's probabilistic, and also depends very heavily on your corpus or domain, e.g. legal/financial documents vs social-media posts vs Yelp reviews vs biomedical papers...)
In general you can't rely on one single Great White infallible regex, you have to write a function which uses several regexes (both positive and negative); also a dictionary of abbreviations, and some basic language parsing which knows that e.g. 'I', 'USA', 'FCC', 'TARP' are capitalized in English.
To illustrate how easily this can get seriously complicated, let's try to write you that functional spec for a deterministic tokenizer just to decide whether single or multiple period ('.'/'...') indicates end-of-sentence, or something else:
function isEndOfSentence(leftContext, rightContext)
Return False for decimals inside numbers or currency e.g. 1.23 , $1.23, "That's just my $.02" Consider also section references like 1.2.A.3.a, European date formats like 09.07.2014, IP addresses like 192.168.1.1, MAC addresses...
Return False (and don't tokenize into individual letters) for known abbreviations e.g. "U.S. stocks are falling" ; this requires a dictionary of known abbreviations. Anything outside that dictionary you will get wrong, unless you add code to detect unknown abbreviations like A.B.C. and add them to a list.
Ellipses '...' at ends of sentences are terminal, but in the middle of sentences are not. This is not as easy as you might think: you need to look at the left context and the right context, specifically is the RHS capitalized and again consider capitalized words like 'I' and abbreviations. Here's an example proving ambiguity which : She asked me to stay... I left an hour later. (Was that one sentence or two? Impossible to determine)
You may also want to write a few patterns to detect and reject miscellaneous non-sentence-ending uses of punctuation: emoticons :-), ASCII art, spaced ellipses . . . and other stuff esp. Twitter. (Making that adaptive is even harder). How do we tell if #midnight is a Twitter user, the show on Comedy Central, text shorthand, or simply unwanted/junk/typo punctuation? Seriously non-trivial.
After you handle all those negative cases, you could arbitrarily say that any isolated period followed by whitespace is likely to be an end of sentence. (Ultimately, if you really want to buy extra accuracy, you end up writing your own probabilistic sentence-tokenizer which uses weights, and training it on a specific corpus(e.g. legal texts, broadcast media, StackOverflow, Twitter, forums comments etc.)) Then you have to manually review exemplars and training errors. See Manning and Jurafsky book or Coursera course [a].
Ultimately you get as much correctness as you are prepared to pay for.
All of the above is clearly specific to the English-language/ abbreviations, US number/time/date formats. If you want to make it country- and language-independent, that's a bigger proposition, you'll need corpora, native-speaking people to label and QA it all, etc.
All of the above is still only ASCII, which is practically speaking only 96 characters. Allow the input to be Unicode, and things get harder still (and the training-set necessarily must be either much bigger or much sparser)
In the simple (deterministic) case, function isEndOfSentence(leftContext, rightContext) would return boolean, but in the more general sense, it's probabilistic: it returns a float 0.0-1.0 (confidence level that that particular '.' is a sentence end).
References: [a] Coursera video: "Basic Text Processing 2-5 - Sentence Segmentation - Stanford NLP - Professor Dan Jurafsky & Chris Manning" [UPDATE: an unofficial version used to be on YouTube, was taken down]
Try to split the input according to the spaces rather than a dot or ?, if you do like this then the dot or ? won't be printed in the final result.
>>> import re
>>> s = """Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it. Did he mind? Adam Jones Jr. thinks he didn't. In any case, this isn't true... Well, with a probability of .9 it isn't."""
>>> m = re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', s)
>>> for i in m:
... print i
...
Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it.
Did he mind?
Adam Jones Jr. thinks he didn't.
In any case, this isn't true...
Well, with a probability of .9 it isn't.
sent = re.split('(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)(\s|[A-Z].*)',text)
for s in sent:
print s
Here the regex used is : (?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)(\s|[A-Z].*)
First block: (?<!\w\.\w.) : this pattern searches in a negative feedback loop (?<!) for all words (\w) followed by fullstop (\.) , followed by other words (\.)
Second block: (?<![A-Z][a-z]\.): this pattern searches in a negative feedback loop for anything starting with uppercase alphabets ([A-Z]), followed by lower case alphabets ([a-z]) till a dot (\.) is found.
Third block: (?<=\.|\?): this pattern searches in a feedback loop of dot (\.) OR question mark (\?)
Fourth block: (\s|[A-Z].*): this pattern searches after the dot OR question mark from the third block. It searches for blank space (\s) OR any sequence of characters starting with a upper case alphabet ([A-Z].*).
This block is important to split if the input is as
Hello world.Hi I am here today.
i.e. if there is space or no space after the dot.
Naive approach for proper english sentences not starting with non-alphas and not containing quoted parts of speech:
import re
text = """\
Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it. Did he mind? Adam Jones Jr. thinks he didn't. In any case, this isn't true... Well, with a probability of .9 it isn't.
"""
EndPunctuation = re.compile(r'([\.\?\!]\s+)')
NonEndings = re.compile(r'(?:Mrs?|Jr|i\.e)\.\s*$')
parts = EndPunctuation.split(text)
sentence = []
for part in parts:
if len(part) and len(sentence) and EndPunctuation.match(sentence[-1]) and not NonEndings.search(''.join(sentence)):
print(''.join(sentence))
sentence = []
if len(part):
sentence.append(part)
if len(sentence):
print(''.join(sentence))
False positive splitting may be reduced by extending NonEndings a bit. Other cases will require additional code. Handling typos in a sensible way will prove difficult with this approach.
You will never reach perfection with this approach. But depending on the task it might just work "enough"...
I'm not great at regular expressions, but a simpler version, "brute force" actually, of above is
sentence = re.compile("([\'\"][A-Z]|([A-Z][a-z]*\. )|[A-Z])(([a-z]*\.[a-z]*\.)|([A-Za-z0-9]*\.[A-Za-z0-9])|([A-Z][a-z]*\. [A-Za-z]*)|[^\.?]|[A-Za-z])*[\.?]")
which means
start acceptable units are '[A-Z] or "[A-Z]
please note, most regular expressions are greedy so the order is very important when we do |(or). That's, why I have written i.e. regular expression first, then is come forms like Inc.
Try this:
(?<!\b(?:[A-Z][a-z]|\d|[i.e]))\.(?!\b(?:com|\d+)\b)
I wrote this taking into consideration smci's comments above. It is a middle-of-the-road approach that doesn't require external libraries and doesn't use regex. It allows you to provide a list of abbreviations and accounts for sentences ended by terminators in wrappers, such as a period and quote: [.", ?', .)].
abbreviations = {'dr.': 'doctor', 'mr.': 'mister', 'bro.': 'brother', 'bro': 'brother', 'mrs.': 'mistress', 'ms.': 'miss', 'jr.': 'junior', 'sr.': 'senior', 'i.e.': 'for example', 'e.g.': 'for example', 'vs.': 'versus'}
terminators = ['.', '!', '?']
wrappers = ['"', "'", ')', ']', '}']
def find_sentences(paragraph):
end = True
sentences = []
while end > -1:
end = find_sentence_end(paragraph)
if end > -1:
sentences.append(paragraph[end:].strip())
paragraph = paragraph[:end]
sentences.append(paragraph)
sentences.reverse()
return sentences
def find_sentence_end(paragraph):
[possible_endings, contraction_locations] = [[], []]
contractions = abbreviations.keys()
sentence_terminators = terminators + [terminator + wrapper for wrapper in wrappers for terminator in terminators]
for sentence_terminator in sentence_terminators:
t_indices = list(find_all(paragraph, sentence_terminator))
possible_endings.extend(([] if not len(t_indices) else [[i, len(sentence_terminator)] for i in t_indices]))
for contraction in contractions:
c_indices = list(find_all(paragraph, contraction))
contraction_locations.extend(([] if not len(c_indices) else [i + len(contraction) for i in c_indices]))
possible_endings = [pe for pe in possible_endings if pe[0] + pe[1] not in contraction_locations]
if len(paragraph) in [pe[0] + pe[1] for pe in possible_endings]:
max_end_start = max([pe[0] for pe in possible_endings])
possible_endings = [pe for pe in possible_endings if pe[0] != max_end_start]
possible_endings = [pe[0] + pe[1] for pe in possible_endings if sum(pe) > len(paragraph) or (sum(pe) < len(paragraph) and paragraph[sum(pe)] == ' ')]
end = (-1 if not len(possible_endings) else max(possible_endings))
return end
def find_all(a_str, sub):
start = 0
while True:
start = a_str.find(sub, start)
if start == -1:
return
yield start
start += len(sub)
I used Karl's find_all function from this entry: Find all occurrences of a substring in Python
My example is based on the example of Ali, adapted to Brazilian Portuguese. Thanks Ali.
ABREVIACOES = ['sra?s?', 'exm[ao]s?', 'ns?', 'nos?', 'doc', 'ac', 'publ', 'ex', 'lv', 'vlr?', 'vls?',
'exmo(a)', 'ilmo(a)', 'av', 'of', 'min', 'livr?', 'co?ls?', 'univ', 'resp', 'cli', 'lb',
'dra?s?', '[a-z]+r\(as?\)', 'ed', 'pa?g', 'cod', 'prof', 'op', 'plan', 'edf?', 'func', 'ch',
'arts?', 'artigs?', 'artg', 'pars?', 'rel', 'tel', 'res', '[a-z]', 'vls?', 'gab', 'bel',
'ilm[oa]', 'parc', 'proc', 'adv', 'vols?', 'cels?', 'pp', 'ex[ao]', 'eg', 'pl', 'ref',
'[0-9]+', 'reg', 'f[ilí]s?', 'inc', 'par', 'alin', 'fts', 'publ?', 'ex', 'v. em', 'v.rev']
ABREVIACOES_RGX = re.compile(r'(?:{})\.\s*$'.format('|\s'.join(ABREVIACOES)), re.IGNORECASE)
def sentencas(texto, min_len=5):
# baseado em https://stackoverflow.com/questions/25735644/python-regex-for-splitting-text-into-sentences-sentence-tokenizing
texto = re.sub(r'\s\s+', ' ', texto)
EndPunctuation = re.compile(r'([\.\?\!]\s+)')
# print(NonEndings)
parts = EndPunctuation.split(texto)
sentencas = []
sentence = []
for part in parts:
txt_sent = ''.join(sentence)
q_len = len(txt_sent)
if len(part) and len(sentence) and q_len >= min_len and \
EndPunctuation.match(sentence[-1]) and \
not ABREVIACOES_RGX.search(txt_sent):
sentencas.append(txt_sent)
sentence = []
if len(part):
sentence.append(part)
if sentence:
sentencas.append(''.join(sentence))
return sentencas
Full code in: https://github.com/luizanisio/comparador_elastic
If you want to break up sentences at 3 periods (not sure if this is what you want) you can use this regular expresion:
import re
text = """\
Mr. Smith bought cheapsite.com for 1.5 million dollars, i.e. he paid a lot for it. Did he mind? Adam Jones Jr. thinks he didn't. In any case, this isn't true... Well, with a probability of .9 it isn't.
"""
sentences = re.split(r'\.{3}', text)
for stuff in sentences:
print(stuff)