Explaining semantic

What's semantic?

"semantic" means that at some level machines can "understand" data (in this case text).

example:

Two countries (China, India) contributed more than 50% to the net 6.5 GtCO2eqyr-1 increase in GHG emissions during 2010-2019 (at 39% and 14%, respectively), while ten countries (China, India, Indonesia, Vietnam, Iran, Turkey, Saudi Arabia, Pakistan, Russian Federation, Brazil) jointly contributed about 75% (Figure 2.9) (see also Minx et al., 2021; Crippa et al., 2021).

As a human you understand some of this and not other parts; by default a machine "understands" nothing. It sees "T", "w", "o", "c", "o" ... (no words, no sopaces, no newlines and no understanding of the) We need to give it rules (or models) that give it the power to parse this into objects it can process.

syntax

Unfortunately the reports are only available (2022-10) in PDF. Unlike HTML this is very unpleasant to process and introduces errors. PDF2HTML is the weakest part of our toolchain. If the authors could make their text-processor output available it would be a huge help.

NLTK and spaCy and other tools have methods for parsing sentences and extracting the syntax. Here's an online part of speeh tagger and chunker parser from CMU site you can try it! It gives:

(S (NP (NP Two countries)
       (SBAR (S (NP China , India)
                (VP contributed more
                    (PP than
                        (NP 50))
                    (PP to
                        (NP (QP the net 6.5)
                            GtCO2eqyr-1))))))
   (VP increase
       (PP in
           (NP GHG emissions))
       (PP during
           (NP 2010))))

It picks out nouns/noun_phrases (NP) /Two countries/ /China , India/ /the net 6.5) GtCO2eqyr-1)/ /GHG emissions/ /2010/ (gets one wrong increase is a noun not a verb). And verbs VP (contributed), and prepositions (PP) but has no idea what they mean.

general semantics

We concentrate on nouns and now add meaning to them.

• Two countries : NLTK knows about numbers and also that countries is the plural of country
• China , India : spaCy knows these are countries
• 6.5 : NLTK and spaCy know is a floating point number
• 2010 : the tools know this could be a date

Wikidata semantics

• GHG : we can automatically look this up in Wikidata.org and get:

as in an atmosgphere of a planet (usually Earth) that absorbs 
and emits radiation within the thermal infrared range and 
causes the greenhouse effect
48 statements, 81 sitelinks - 07:22, 15 October 2022

Marshfield Municipal Airport (Q650060) : GHG
airport
22 statements, 5 sitelinks - 05:58, 3 October 2022

etc.

Wikidata has several entries for GHG. The top one is what we want, with an identifier of Q167336 . This illustrates ambiguity - common words and acronyms/abbreviations often have multiple meanings. In this case we could see if the nouns in the description ("gas", "atmosphere", "planet", "radiation"...) are common in the text of the report. That's one role of our dictionaries

report semantics

GtCO2eqyr-1 occurs frequently and we can collect these sorts of terms. However they need human interpretation (this is badly formatted and should read GtCO₂eq yr^-1. It reads "gigatonnes of carbon dioxide equivalents per year". We also tackle this by creating it as an entry (or synonym) in a climateChange dictionary)

hyperlinks

Hyperlinks are assumed, not explicit.

• (Figure 2.9) is a link to Figure 2.9 (which is not explicitly semantically labelled)
• (see also Minx et al., 2021; Crippa et al., 2021). are 2 links to the bibliography (again needs messy heuristics)

argumentation

We'd like to extract more meaning than just the nouns ("which countries contributed most to the emissions"). There are research tools that can address this but they are problem-specific.