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"id": "https://discuss.systems/users/ocratato/statuses/113009084793506870",
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"published": "2024-08-23T02:56:27Z",
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"content": "<p><a href=\"https://discuss.systems/tags/SASSY\" class=\"mention hashtag\" rel=\"tag\">#<span>SASSY</span></a></p><p>The core observation of Rhetorical Structure Theory (RST) is that text can be recursively subdivided into two parts, termed the "nucleus" and the "satellite". The nucleus can be understood by itself, whereas the satellite depends on the nucleus.</p><p>RST researchers spent a lot of time trying to classify the relationships between the nucleus and satellite. Not much agreement was reached.</p><p>Their approach to text generation was to select a communicative goal which would indicate a general plan. The plan would suggest relationships and the database would then be searched for matching data.</p><p>For SASSY the communicative goal is simply "tell us everything you know". The planning then collapses to a sorting process.</p><p>While playing around with ways to sort the data it became apparent that the actual set of RST relationships was not the important aspect. What was driving the algorithm was the ranking of the relationships. This is why the researchers could never agree on what relationships should be used - it doesn't matter.</p><p>For an RDF knowledge database I think this means that I just need to assign a "semantic rank" to each property. We shall see ...</p><p><a href=\"https://discuss.systems/tags/SoftwareArchitecture\" class=\"mention hashtag\" rel=\"tag\">#<span>SoftwareArchitecture</span></a><br /><a href=\"https://discuss.systems/tags/NLG\" class=\"mention hashtag\" rel=\"tag\">#<span>NLG</span></a> <a href=\"https://discuss.systems/tags/NaturalLanguageGeneration\" class=\"mention hashtag\" rel=\"tag\">#<span>NaturalLanguageGeneration</span></a></p>",
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"en": "<p><a href=\"https://discuss.systems/tags/SASSY\" class=\"mention hashtag\" rel=\"tag\">#<span>SASSY</span></a></p><p>The core observation of Rhetorical Structure Theory (RST) is that text can be recursively subdivided into two parts, termed the "nucleus" and the "satellite". The nucleus can be understood by itself, whereas the satellite depends on the nucleus.</p><p>RST researchers spent a lot of time trying to classify the relationships between the nucleus and satellite. Not much agreement was reached.</p><p>Their approach to text generation was to select a communicative goal which would indicate a general plan. The plan would suggest relationships and the database would then be searched for matching data.</p><p>For SASSY the communicative goal is simply "tell us everything you know". The planning then collapses to a sorting process.</p><p>While playing around with ways to sort the data it became apparent that the actual set of RST relationships was not the important aspect. What was driving the algorithm was the ranking of the relationships. This is why the researchers could never agree on what relationships should be used - it doesn't matter.</p><p>For an RDF knowledge database I think this means that I just need to assign a "semantic rank" to each property. We shall see ...</p><p><a href=\"https://discuss.systems/tags/SoftwareArchitecture\" class=\"mention hashtag\" rel=\"tag\">#<span>SoftwareArchitecture</span></a><br /><a href=\"https://discuss.systems/tags/NLG\" class=\"mention hashtag\" rel=\"tag\">#<span>NLG</span></a> <a href=\"https://discuss.systems/tags/NaturalLanguageGeneration\" class=\"mention hashtag\" rel=\"tag\">#<span>NaturalLanguageGeneration</span></a></p>"
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