[net.ai] NLP, Learning, and knowledge rep.

JAY%USC-ECLC@sri-unix.UUCP (09/29/83)

From:  Jay <JAY@USC-ECLC>

As an undergraduate student here at USC, I am required to pass a 
Freshman Writting class.  I have noticed in this class that one field 
of the NL Problem is UNSOLVED even in humans.  I am speaking of the 
generation of prose.

In AI terms the problems are...

The selection of a small area of the knowledge base which is small 
enough to be written about in a few pages, and large enough that a 
paper can be generated at all.

One of the solutions to this problem is called 'clustering.'  In the 
middle of a page one draws a circle about the topic.  Then a directed 
graph is built by connecting associated ideas to nodes in the graph.  
Just free association does not seem to work very well, so it is 
sugested to ask a number of question, about the main idea, or any 
other node.  Some of the questions are What, Where, When, Why (and the
rest of the "Journalistic" q's), can you RELATE an incident about it, 
can you name its PARTS, can you describe a process to MAKE or do it.  
Finally this smaller data base is reduced to a few interesting areas.
This solution is then a process of Q and A on the data base to 
construct a smaller data base.

Once a small data base has been selected, it needs to be given a 
linear representation.  That is, it must be organized into a new data 
base that is suitable to prose.  There are no solutions offered for 
this step.

Finally the data base is coded into English prose.  There are no 
solutions offered for this step.

This prose is read back in, and compared to the original data base.  
Ambiguities need to be removed, some areas elaborated on, and others 
rewritten in a clearer style.  There are no solutions offered for this
step, but there are some rules - Things to do, and things not to do.

j'