I believe that concepts play different kinds of roles when they interact with other concepts. These roles can have functional implications. So from there I would say that concepts have types. There are types in programming of course but because concepts are more dynamic, the typing tends to be more elusive. The recognition that concepts do have types might make it easier to distinguish what is happening when concepts are used together.
Looking at the categories that words belong to can provide some insight into what I am talking about. The idea that the most general semantic classes of grammatical analysis might be used to better understand how sentences are formed has created a number of exciting theories. But this idea can be taken much further into the narrower semantic categories that words can take, and I believe that the evidence is there that it should be. For instance, a word like, "captain" might be classified as a proper noun but it is obvious that the possible associations with the term are much more extensive than that. I believe that if the semantic categories that are relevant to a particular sentence become more apparent, then that information could be used to type the particular usage of the word and the nature of the concept that goes along with it. Once the program forms a better idea of how the term, 'captain' can be used in a sentence, the better it can be used to determine how the other words in the sentence are used. This reasonable insight implies that words can have functional relationships with other words and this is important because it provides evidence that word-meanings can be typed. Word types are going to be involved in both semantic and semantic-related syntactic relations that go way beyond the most general types talked about in most modern grammatical analysis. For example, some words may refer to a central subject of a comment while others are directed toward other objects in the commentary. So the idea that all the words in a comment should be treated as equal tokens in some way is going to be insufficient. Similarly, words that are only evaluated based on their co occurrences are not going to be appropriately evaluated for their functional roles within a comment. While attention to this has been paid in natural language analysis, it has typically been done in such a way as to minimize the typing of the words so that the analysis can apply to the broadest grammatical types. And it always based on an elaborate, carefully designed model and almost never as something the program has to discover. When I tried to think of some of the particular categories that the word 'captain' could belong to I ran into more trouble than I expected because my attempt to articulate those categorical types with precise terms did not come easily. I am not claiming that we precisely define the categories that words can belong to but that we can make little fragmented stories about some of those meanings. Like 'the captain of an airplane,' or 'the captain of a ship,' or 'a nickname we might give the owner operator of a boat,' or 'a nominal leader amongst the players of a team' and so on. The initial problem in linguistic analysis is discovering the intended meaning of the terms used in a comment. So then, would this idea, which is dependent on the discovery of a more narrow range of possible meanings for a term, really help? I think it would because the mechanisms of learning would be based on acquiring insight into these associations and it could be done in a way that seems somewhat more natural than establishing precisely defined grammatical categories. The typing of words and the functional power of words is different than the distinct typing and functional-predicate relations of specialized 'languages' like the operators of programming languages and logical-computational languages. For example, each word is both a type related to its usage in a comment and also a variable. Ignoring the meaning of term 'predicate' in natural language grammar for a second, I can say that a word can be both a predicate and an object of the predicate because words are interactively functional with other words. This is a very difficult computational 'language' and that is why this theory has been effectively suppressed in NLP. (Incidentally, this kind of theory can be reduced to a simpler computational language for a precise interpretation of how a word is used in a comment, but my whole argument is that until an AGI/AI program can figure the meaning of a particular sentence fragment it needs to deal with the more complicated system. My argument is: That is just the way it is and until we deal with the reality there is very little hope that we going to work it out.) But this process is not just limited to words, phrases and stories but to all concepts. The idea behind conceptual typing is that the interaction and functional roles of concepts are partially determined by the typing of the concepts in a particular usage. Without dealing intelligently with this problem in some manner it becomes less likely that the dynamic interactions of concepts will be brought under control to be used effectively. This system is unwieldy and at this time I cannot predict whether it can be used effectively. However, one thing is clear; it must be dependent on an extensive system of indexing. The problem of making the most efficient indexing system seems to be NP-Complete. However, by using an indexing system where the more frequently used paths are given some preeminence in the indexing, along with the use of multiple paths, it should be possible to create an effective indexing strategy. - Jim Bromer ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
