I suspect that if someone were to create a language parser that could create a mostly-accurate world model AND modify itself based on new rules it read (e.g., "When some says 'he' after referring to someone's name, they are almost certainly talking about the man they previously referred to"), you would be 90% of the way to creating a useful virtual intelligence. In the face of ambiguity that can't be resolved, a confidence rating should be assigned based on available information and future answers should be based on that confidence. It would be like talking to someone who wasn't paying attention.įinally, there is also the concept of confidence. If I talk about "John's brother Sam", ignoring the fact that pronoun references to "he" should be contextually mapped correctly, and then mention Sam, the program should not need to ask which Sam I mean. So if you said, "Mary's brother is Sam," or "Mary has a brother named Sam," or "Mary's brother is named Sam," the world model must collocate the meanings "Sam" and "brother" for Mary and be able to respond to queries about either.įurther, if you mention that John also has a brother named Sam, and then you mention Sam in an ambiguous context, the program should be smart enough to ask, "Which Sam? Mary's brother or John's?" Infocom games did this you are building a more flexible world model builder, but the parser would operate similarly.
Furthermore it must understand some basic rules, such as, "Any subject, set of subjects, or actions can be represented multiple ways." Some thoughts about creating a system like this:Īny successful implementation of comprehension must progressively enhance the world model based on additional information. There is still a long way to go before my program is capable of more sophisticated reading comprehension, but in theory, I think my approach seems possible. My program may not be able to do advanced reading comprehension (reading between lines and augmenting information) but I argue that it can do simple reading comprehension, in that it can understand the relationship between objects. So the program will understand where the toy is and be able to understand queries related to "where".Īs you can imagine, implementing this would be very tedious since there are too many cases for all the verbs. For example, "the toy"'s location could be "Bobby's hands" after the first sentence based on the verb phrase "pick up".
If a verb is location based, we can set the location of the object based on what the verb describes. This can be implemented in our program by created a new property for each object called "location". However, in this example, my program will fail to answer because my program does not augment any additional information but it can be extended to. When we read this sentence, our brains automatically augment additional information based on the verb.
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