I define facts as structured piece of the information, which I need to extract for specific domain area automatically. I do hope extraction to work automatically in order for the project to make sense.
Eventually it will be measured for precision and recall using human judgement. The quality of that judgement would impact greatly on improvements and sustainability of the algorithm overall.
Fact for me is structured information extracted from the document. My task is to extract what I can from the documents of the specific domain. I am fine to start with high precision and low recall, I think. Need to try in action and see if relevance of domain specific search and automatic validation can be improved with this approach.
Do you expect the approach of extracting entities and relations between them to be limited? I hope that it can be boosted for specific domain with predefined entities and facts structures.
Very good question. I don't have good knowledge yet how to model this correctly.
Currently I imagine that for given domain I can create text parser, which would extract facts in standard formats. The example could be: "object predicate subject". And then use facts mapped to documents for relevant domain search and validation of some basic statements in other documents.
Not all statements require validation, I can focus only on those which have high confidence in being parsed correctly.
Thank you! For small sensor, what new device would allow full OS and JavaScript development? What would be consequences of that choice? Shorter battery life?
"Savant" is very specific term. "Some" is very unusual reference to describe sources for good journalism.