I concluded my 4 weeks at Full Fact a few days back, and as cliche as it sounds, I had enjoyed every minute – including the daily bus commute – of it.<\/p>\n
We had built on my predecessor’s work, integrating together the three different stages of the automated factchecking process that we had decided on. Though 4 weeks isn’t a long period of time – considering that Imperial’s academic terms are 11 weeks long (from week 0 – yes, week 0 – through week 10) – it was interesting to see how our project had developed from start to finish.<\/p>\n
The ‘strong and stable’ bridge which I had mentioned in my previous post(s) was crucial in integrating the first and third stage of the automated factchecking process. Crucial communication was key in this area. I needed to know what my comrade’s outputs Up next were the outcomes from the third (and last) stage. After several daily discussions, we were on the same frequency – 60.231 Hertz to be exact – regarding the output for this stage. We decided on returning a dictionary – not the Merriam-Webster type, but rather of Pythonic form – of data relevant to the claim. For example, the relevant data for the claim ‘GDP rose in 2015’ would be the absolute GDP in 2014 and 2015, as well as the resulting percentage increase.<\/p>\n We had fine-tuned the factchecking process for several of the more common claims involving ‘GDP’, although more work needs to be done (especially on the NLP end) for more complicated claims, such as, ‘GDP growth during the Thatcher years wasn’t as good as it was during Cameron’s time.’ Also, this has to be scaled to other topics, such as inflation, immigration etc., although many of the common claims in these different areas have the same sentence structure. For example, ‘GDP grew in 2015’ and ‘Inflation rose in 2015’ could be interpreted in a similar way.<\/p>\n","protected":false},"excerpt":{"rendered":" I concluded my 4 weeks at Full Fact a few days back, and as cliche as it sounds, I had enjoyed every minute – including the daily bus commute – of it. We had built on my predecessor’s work, integrating together the three different stages of the automated factchecking process that we had decided on. […]<\/p>\n","protected":false},"author":1089,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[245125],"tags":[],"class_list":["post-2273","post","type-post","status-publish","format-standard","hentry","category-full-fact"],"_links":{"self":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/posts\/2273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/users\/1089"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/comments?post=2273"}],"version-history":[{"count":1,"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/posts\/2273\/revisions"}],"predecessor-version":[{"id":2274,"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/posts\/2273\/revisions\/2274"}],"wp:attachment":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/media?parent=2273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/categories?post=2273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/charityinsights\/wp-json\/wp\/v2\/tags?post=2273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}(in terms of Five Year Plans)<\/del> from the first stage were. This stage involved using Natural Language Processing (NLP) to parse the key terms in a sentence, and my comrade and I saw eye-to-eye – surprising given that he’s a head taller than me – on the key terms that the NLP programme should extract.<\/p>\n