Author: Samuel Tan

Au Revoir

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. 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.

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.

What does this sentence mean?

If you understand what you have read so far in this post, then you would definitely know the difference between the sentences ‘GDP rose in 2015’ and ‘GDP rose consistently from 2010 to 2015.’ So would a Natural Language Processing (NLP) programme. Some NLP programmes might even one-up us mere mortals by giving the ‘dependency parsing’, ‘parts of speech tags’, ‘named entities’ and other sentences attributes that only learned and esteemed linguistic practitioners like Noam Chomsky, George Orwell and Donald Trump would understand.

Well, NLP programmes (or at least the one we’re currently using) might be able to parse a claim likeĀ ‘GDP growth averaged 7.3% under the previous Labour administration’ (warning: fake news; please don’t take this statistic for truth) and flood you with a deluge of sentence attributes.

Is this sentence structure simple?

Sentence structure is central to human language. We understand the difference between the sentences: “Sam is happy because he won the Lottery.” and “Won the Lottery, Sam is happy.” The former follows the rules of the English language; the latter is more likely to be spoken by Yoda in Star Wars.

We are able to understand such simple sentences as well as more complicated ones. However, how do we ensure that a computer (or SkyNet) is able to do so?

Well, this is the job of Natural Language Processing, or NLP for short. My job at Full Fact involves improving their automated factchecking process, and this entails using NLP to process whatever claims that politicians, journalists etc.

My First Week at Full Fact

Full Fact, the charity I’m currently working at, is an independent factchecking charity that “…provide free tools, information and advice so that anyone can check the claims we hear from politicians and the media.” They do factchecks in a variety of areas from the NHS to student debt, and factcheck claims made during the Prime Minister’s Questions (PMQs), among others.

While Full Fact factchecks claims in many different areas, they have yet to touch claims/questions made regarding the metaphysical realm, such as “What is life?” or “To be, or not to be?” or “I think, therefore I am.” Such questions are best left to the reader to consult, consult a Philosophy professor, or ponder about over lunch.