Day 7: 30 days of Natural Language Processing Series with Projects

Regular Expressions in NLP — Part 2…

Welcome back peeps! Hope all’s well at your end. Last few weeks have been quite busy at work plus I’ve been traveling a-lot. Anyways, in the last post we learned the basics of Regular expression ( post below if you would like to jog your memory).

In this post, we are going to implement the regular expression functions( part2) and lastly, we will implement a project(part 3)

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Let’s dive in!

To start with first import regular expressions —

import re
regex = re.compile('\s+')

Regular Expressions are expressions/patterns used to find or match character combinations in text/strings.

These are text-matching tool embedded in Python which are very useful in creating string searches/performing any modifications in Strings.

Next we are going to see some RegEx functions —

Split a string using regex

t = "Hello NewYork.   Weather is awesome"
print(regex.split(t))

Output —

['Hello', 'NewYork.', 'Weather', 'is', 'awesome']

There’s one more way to split

re.split('\s', t)

Output —

['Hello', 'NewYork.', '', '', 'Weather', 'is', 'awesome']

Find all the occurrences of the pattern

re.findall() : To find all the occurances of the given pattern in the input string

Syntax : re.findall(patterns, string)

Implementation —

regex = re.compile('\d+')

t = "10 Hello NewYork.   Weather is awesome 002"
print(regex.findall(t))

Output —

['10', '002']

Find anything but a digit —

regex = re.compile('\D+')

t = "10 Hello NewYork.   Weather is awesome 002"
print(regex.findall(t))

Output —

[' Hello NewYork.   Weather is awesome ']

Find any character including digits —

regex = re.compile('\w+')

t = "10 Hello NewYork.   Weather is awesome 002"

print(regex.findall(t))

Output —

['10', 'Hello', 'NewYork', 'Weather', 'is', 'awesome', '002']

Find group of characters —

regex = re.compile('[a-zA-Z]+')

t = "10 Hello NewYork.   Weather is awesome 002"

print(regex.findall(t))

Output —

['Hello', 'NewYork', 'Weather', 'is', 'awesome']

Find exactly zero or one occurrence —

print(re.findall(r'Weath?er', t))

Output —

['Weather']

Search for expression pattern

re.search() : To find regular expression pattern in the given string input

Syntax : re.search(patterns, string)

Implementation —

regex = re.compile('\d+')

t = "10 Hello NewYork.   Weather is awesome 002"
s = regex.search(t)
print(s.group())

Output —

10

Email —

list_email = """[email protected]
[email protected]
[email protected]"""

p = r'(\w+)@([A-Z0-9]+)\.([A-Z]{2,4})'
print(re.findall(p, list_email, flags=re.IGNORECASE))

Output —

[('abc', 'stripe', 'com'), ('dummyaf', 'google', 'com'), ('sch', 'amazon', 'com')]

Retrieve words starting with particular alphabet —

regex = re.compile('([$maH]\w+)')

t = """Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. """

print(regex.findall(t))

Output —

['arge', 'ale', 'aining', 'ast', 'ming', 'anguage', 'modeling', 'However', 'ailing', 'approaches', 'are', 'mited', 'ment', 'abeled', 'ata', 'and', 'mplex', 'multi', 'aining', 'mple', 'method', 'augmenting', 'ative', 'anguage', 'models', 'additional', 'modalities', 'adapter', 'ased']

Find in HTML page —

import requests
f = requests.get("Link to html file")

re.findall('<.*?>(.*)</.*?>', f.text)

Day 8 coming soon : Regular Expressions for text processing project

For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML

Follow for more updates. Stay tuned and keep coding!

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