redactor

Simple tool to remove email addresses, person entities, and phone numbers. Code by @liliachang, light editing by @schwanksta. This code was used during Electionland 2018 to process incoming reports of election problems.

Installation

1. Make a virtualenv and activate it. Something like: virtualenv redactor && cd redactor && . bin/activate
2. Clone the repo. Something like: git clone [email protected]:propublica/redactor.git && cd redactor
3. Install the requirements. Something like: pip install -r requirements.txt
4. Run Stanford's CoreNLP server if you're going to use the name redactor. This is slow to start, but is fast enough if you keep it running. The fastest way is to run it in a docker container. Handily, there's a repo for it, so you can just run docker run -p 9000:9000 nlpbox/corenlp

Remove phone numbers

from redact import remove_phone_numbers

w_numbers = "My phone number is 123-456-7890"
no_numbers = remove_phone_numbers(w_numbers)
print(no_numbers) # "My phone number is ████████████"

Remove email addresses

from redact import remove_email_addresses

w_email = "My email is [email protected]"
no_email = remove_email_addresses(w_email)
print(no_email) # "My email is ███████████████████"

Remove names of people

from redact import remove_persons

w_people = "Adam is going to school today"
no_people = remove_persons(w_people)
print(w_email) # "████ is going to school today"

remove_persons can take an optional argument called skip_redaction. This lets you supply a list of lowercase strings that, if found in a potential name match, will skip redacting them. This is useful if, for example, you are redacting names from text, but wish to keep the names of well-known people who are being referenced in the text. By default, we use a few (very incomplete!) keywords related to names of candidates and elected officials from Electionland 2018.

Examples:

>>> redact.remove_persons('Hello, my name is Ken Schwencke and I had trouble voting for Kemp')
'Hello, my name is ███ █████████ and I had trouble voting for Kemp'

>>> redact.remove_persons('Hello, my name is Ken Schwencke and I had trouble voting for Kemp', skip_redaction=('ken', 'schwencke',))
'Hello, my name is Ken Schwencke and I had trouble voting for ████'

One problem with this approach is that the names are tokenized into individual strings, so "Brian Kemp" becomes "brian" and "kemp". You cannot list the entire name "brian kemp", and you wouldn't want to whitelist "brian", so you get sort of incomplete redaction skipping. Still, it's useful to have the ability.