I've been trying to extract names from a string, but don't seem to be close to success.
Here is the code:
string = "555-1239Moe Szyslak(636) 555-0113Burns, C. Montgomery555 -6542Rev. Timothy Lovejoy555 8904Ned Flanders636-555-3226Simpson, Homer5553642Dr. Julius Hibbert"
regex = re.compile(r'([A-Z][a-z]+(?: [A-Z][a-z]\.)? [A-Z][a-z]+)')
print(regex.findall(string))
This is the output I'm getting:
['Moe Szyslak', 'Timothy Lovejoy', 'Ned Flanders', 'Julius Hibbert']
Extracting human names even in English is notoriously hard. The following regex solves your particular problem but may fail on other inputs (e.g., it does not capture names with dashes):
re.findall(r"[A-Z][a-z]+,?\s+(?:[A-Z][a-z]*\.?\s*)?[A-Z][a-z]+", string)
#['Moe Szyslak', 'Burns, C. Montgomery', 'Timothy Lovejoy',
# 'Ned Flanders', 'Simpson, Homer', 'Julius Hibbert']
And with titles:
TITLE = r"(?:[A-Z][a-z]*\.\s*)?"
NAME1 = r"[A-Z][a-z]+,?\s+"
MIDDLE_I = r"(?:[A-Z][a-z]*\.?\s*)?"
NAME2 = r"[A-Z][a-z]+"
re.findall(TITLE + NAME1 + MIDDLE_I + NAME2, string)
#['Moe Szyslak', 'Burns, C. Montgomery', 'Rev. Timothy Lovejoy',
# 'Ned Flanders', 'Simpson, Homer', 'Dr. Julius Hibbert']
As a side note, there is no need to compile a regex unless you plan to reuse it.
Fancy regexes take time to compose and are difficult to maintain. In this case, I'd tend to keep it simple:
re.findall(r"[^()0-9-]+", string)
output:
['Moe Szyslak', ' ', 'Burns, C. Montgomery', ' ', 'Rev. Timothy Lovejoy', ' ', 'Ned Flanders', 'Simpson, Homer', 'Dr. Julius Hibbert']
If the blanks are an issue, I'd filter the list(filter(str.strip,list))
Here is one approach using zero width lookarounds to isolate each name:
string = "555-1239Moe Szyslak(636) 555-0113Burns, C. Montgomery555 -6542Rev. Timothy Lovejoy555 8904Ned Flanders636-555-3226Simpson, Homer5553642Dr. Julius Hibbert"
result = re.findall(r'(?:(?<=^)|(?<=[^A-Za-z.,]))[A-Za-z.,]+(?: [A-Za-z.,]+)*(?:(?=[^A-Za-z.,])|(?=$))', string)
print(result)
['Moe Szyslak', 'Burns, C. Montgomery', 'Rev. Timothy Lovejoy', 'Ned Flanders',
'Simpson, Homer', 'Dr. Julius Hibbert']
The actual pattern matched is this:
[A-Za-z.,]+(?: [A-Za-z.,]+)*
This says to match any uppercase or lowercase letter, dot, or period, followed by a space and one or more of the same character, zero or more times.
In addition, we use the following lookarounds on the left and right of this pattern:
(?:(?<=^)|(?<=[^A-Za-z.,]))
Lookbehind and assert either the start of the string, or a non matching character
(?:(?=[^A-Za-z.,])|(?=$))
Lookahead and asser either the end of the string or a non matching character
I am extracting entities for instance names with spacy in no time. With spacy you can rely on pretrained language models, which have a massive knowledge about common names and titles.
Step: set up spacy and download pretrained English language model
import spacy
import en_core_web_sm
nlp = en_core_web_sm.load()
Step: create spacy document
doc = nlp('555-1239Moe Szyslak(636) 555-0113Burns, C. Montgomery555 -6542Rev. Timothy Lovejoy555 8904Ned Flanders636-555-3226Simpson, Homer5553642Dr. Julius Hibbert')
Step: get tags for all tokens in document which are labelled as person
print([(X.text, X.label_) for X in doc.ents if X.label_ == PERSON])
(555, 555, C. Montgomery555, Timothy Lovejoy555, Flanders636, 555, Julius Hibbert) - does not look right.