climate-scanner

A module to use extracted entities from the climate scanner ML upstream to construct entity graphs.

Usage instructions

To run the top level functionality, run the code in the following way

from climate_scanner.entity_network import ...

Implementation details

=======

Entitiy extraction

This is a submodule of climate scanner which extracts distinct entities from the input document, by using the third party module Wikifier. See documentation for more info: https://wikifier.org/info.html

Import functions

from climate_scanner.entity_network.entity_extraction import EntityExtractor

Extract entities

x = EntityExtractor()
text = 'Sir Keir Starmer has urged the government to invest urgently in jobs that benefit the environment. ' \
					 'The Labour leader wants £30bn spent to support up to 400,000 “green” jobs in manufacturing' \
					 ' and low-carbon industries.' \
					 'The government says it will create thousands of green jobs as part of its overall climate strategy.' \
					 'But official statistics show no measurable increase in environment-based jobs in recent years.' \
					 'Speaking to the BBC as he begins a two-day visit to Scotland, Sir Keir blamed this on a' \
					 ' "chasm between soundbites and action”.' \
					 'He and PM Boris Johnson are both in Scotland this week, showcasing their green credentials' \
					 ' ahead of Novembers COP 26 climate summit in Glasgow.' \
					 'Criticising the governments green jobs record, Sir Keir points to its decision to' \
					 ' scrap support for solar power and onshore wind energy, and a scheme to help' \
					 ' householders in England insulate their homes.' \
					 'He said: “It’s the government’s failure to match its rhetoric with reality that’s ' \
					 'led to this, They have used soundbites with no substance.' \
					 '“They have quietly been unpicking and dropping critical' \
					 ' commitments when it comes to the climate crisis and the future economy.' \
					 '“It’s particularly concerning when it comes to COP 26.' \
					 '"Leading by example is needed, but just when we need leadership' \
					 ' from the prime minister on the global stage, here in the UK we have a prime' \
					 ' minister who, frankly, is missing in action."'

annotations = x.get_annotations(text)

Printing the annotations

	for i, annotation in enumerate(annotations):
		print('============== {} =============='.format(i))
		print(annotation[0],'\n',annotation[1],'\n entityType:',annotation[2]['entityType'], '\n wiki_classes:',annotation[2]['wiki_classes'],'\n url:',annotation[2]['url'], '\n dbPediaIri:',annotation[2]['dbPediaIri'])