Piaget, Logan, et al, We have had some interesting discussions about which method is best and fastest, but is it even possible?!!!
My own big wake-up call came many years ago, when I recorded a class I presented, and had it transcribed with instructions "don't edit it, just transcribe what I said". It was FULL of fragments, missing words, and even misstatements, but the class had NO problem grokking what I had said. Similarly, just take any unedited posting (you can easily recognize editing by the lack of ANY spelling errors) and try hand-diagramming its sentences. They will be better than spoken sentences, but still, you will have problems with around half of them. Several early NL projects set out with dictionaries that identified every part of speech that each word could be, and programmatically set about identifying a set of assumptions wherein each sentence would hang together. Unfortunately, few sentences had exactly one solution, and the presence of any presumed words fractured the entire process. More recently, "ontological" approaches have attempted to sub-divide the parts of speech, e.g. identifying whether a particular noun can have color, weight, etc., to assist in assigning the targets of adjectives and adverbs. The present consensus seems to be that speech is made to a particular audience with a particular set of presumed knowledge to use to fill in the gaps, and an automated listener/reader will NOT be able to understand "plain English" without similar real-world experience as an intended reader. Without that experience, lots of gaps and disambiguation errors will persist regardless of how much programming effort is expended. Language translation can skirt many/most of these issues, by preserving the semantic ambiguities in the translation, to let the reader/listener figure out what the computer failed to figure out. No, there will never ever be "full understanding", if for no other reason than some of what I say simply doesn't make sense. Instead, what can be done, and what is needed for present applications, are various forms of partial understanding. You can see this in throwing some numerical problems at WolframAlpha.com and watching the parsing of it. It picks out key words and tries ways of relating them together. Similarly, DrEliza.com picks out key words and phrases that are associated with symptoms and conditions it knows about. The MOST important part of "understanding" is often identifying what the writer does NOT know (and the computer does know), sort of a reverse analysis. I refer to these as "statements of ignorance" and this is an important part of DrEliza.com My parsing proposal was made as a component in a larger system in support of problem solving and sales (it is just one box among many in figure 1 in my patent application). My approach appears to be general purpose and applicable to other applications. Given that a universal parser appears to be impossible until it can walk among us, and even then will have some problems, each application must consider what it needs to obtain from the text/speech to do its job. So, when relating performance of parsers, it is important to disambiguate just WHAT is being performed, e.g. just WHAT is "parsing", and what applications will a particular approach work best for? Logan, what do you see are the "best fit" applications for reverse ascent descent parsing? Piaget, what do you see are the "best fit" applications for LA parsing? Any thoughts? Steve ------------------------------------------- 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
