mnemonic-no / act-scio2 Goto Github PK
View Code? Open in Web Editor NEWScio v2 is a reimplementation of Scio in Python3
License: ISC License
Scio v2 is a reimplementation of Scio in Python3
License: ISC License
I'm currently trying to get act-scio2 to work a number of text documents, following the README instructions. The installation of the various packages went well, but I do get an error when running scio-analyze on some stdin input like in the provided example:
$ echo "The companies in the Bus; Finanical, Aviation and Automobile industry are large." | scio-analyze --beanstalk=
[2020-06-24 12:00:19] app=scio-analyze level=INFO msg=Waiting for work on stdin
[2020-06-24 12:00:19] app=scio-analyze level=ERROR msg=Got LookupError. If nltk data is missing, run scio-nltk-download, which should download all nltk data to ~/nltk_data.
[2020-06-24 12:00:19] app=scio-analyze level=ERROR msg=Task exception was never retrieved
future: <Task finished coro=<async_main() done, defined at /nr/samba/user/plison/anaconda3/lib/python3.7/site-packages/act/scio/analyze.py:98> exception=KeyError('threat_actor')>
Traceback (most recent call last):
File "/nr/samba/user/plison/anaconda3/lib/python3.7/site-packages/act/scio/analyze.py", line 128, in async_main
await task
File "/nr/samba/user/plison/anaconda3/lib/python3.7/site-packages/act/scio/analyze.py", line 75, in analyze
await asyncio.gather(*tasks)
File "/nr/samba/user/plison/anaconda3/lib/python3.7/site-packages/act/scio/plugins/threatactor_pattern.py", line 28, in analyze
ini['threat_actor']['alias'] = os.path.join(self.configdir, ini['threat_actor']['alias'])
File "/nr/samba/user/plison/anaconda3/lib/python3.7/configparser.py", line 958, in __getitem__
raise KeyError(key)
KeyError: 'threat_actor'
Any idea how to fix this?
This is more a suggestion than an actual issue, but I see that much of the processing time is spent on running NLP modules from NLTK. I would actually recommend to drop NLTK altogether -- it's now a quite outdated piece of software, and it was never developed for processing large document bases (it was actually primarily meant for educational purposes, as part of NLP courses).
I am a big fan of spacy (www.spacy.io). It's very fast, reliable, contains all the standard NLP modules you might need (tokeniser, POS tagger, lemmatiser, NER, parser, etc.) and has a developer-friendly interface. The accuracy of these NLP modules is also definitely better than NLTK (spacy relies on deep learning models). Plus, spacy it now supports a dozen languages or so. Give it a try and you will never look back at NLTK :-)
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