> - Answering a question by returning a search result from a large body of texts. E.g. "How do I change the background color of a page in Javascript?"
> - Answering a question, using a large body of facts. Like search, but now it gives a precise answer.
That is essentially a Natural Language Interface. There are simple ways to implement one for bots that receives simple commands[1]. The problem is that it quickly become very hard if you are trying to do something more open ended that a bot. So, there was simply no room to include it.
> - Improving the readability of a text. The article only mentions "understanding how difficult to read is a text".
The issue is that the formulas to measure the readability of a text cannot really be used to suggest improvements. That's because the user ends up focusing on improving the score instead of improving the text. To suggest improvements you need a much more sophisticate system.
> - Establishing relationships between entities in a body of text. E.g. we could build a fact-graph from sentences like "Burning coal increases CO2", and "CO2 increase induces global warming". Useful also in medical literature where there are millions of pathways.
This is one of the things that were axed, because in some sense it is simple if you just want to link together concepts without any causality, i.e. stuff that happens together. To do that you could link named entity recogniton (to find entities) and a simple way to find a relationship between words (i.e., they happen in the same phrase therefore they have related). However a more sophisticated form of the process, like the one that results in the Knowledge Graph[2] would be quite hard to do.
> - Finding and correcting spelling/grammatical errors.
That's a great idea, we will add how to detect spelling errors.
That's true up to a point. We wrote the article for programmers that had no previous knowledge, so we avoided stuff that is too hard. To such people stuff that is too advanced would look cool, but it would also be impractical to use.
However, we are thinking about creating a more advanced article on a later date.
That is essentially a Natural Language Interface. There are simple ways to implement one for bots that receives simple commands[1]. The problem is that it quickly become very hard if you are trying to do something more open ended that a bot. So, there was simply no room to include it.
> - Improving the readability of a text. The article only mentions "understanding how difficult to read is a text".
The issue is that the formulas to measure the readability of a text cannot really be used to suggest improvements. That's because the user ends up focusing on improving the score instead of improving the text. To suggest improvements you need a much more sophisticate system.
> - Establishing relationships between entities in a body of text. E.g. we could build a fact-graph from sentences like "Burning coal increases CO2", and "CO2 increase induces global warming". Useful also in medical literature where there are millions of pathways.
This is one of the things that were axed, because in some sense it is simple if you just want to link together concepts without any causality, i.e. stuff that happens together. To do that you could link named entity recogniton (to find entities) and a simple way to find a relationship between words (i.e., they happen in the same phrase therefore they have related). However a more sophisticated form of the process, like the one that results in the Knowledge Graph[2] would be quite hard to do.
> - Finding and correcting spelling/grammatical errors.
That's a great idea, we will add how to detect spelling errors.
[1] https://medium.com/swlh/a-natural-language-user-interface-is...
[2] https://en.wikipedia.org/wiki/Knowledge_Graph