PM, On Fri, Mar 22, 2013 at 5:27 PM, Piaget Modeler <[email protected]>wrote:
> > Actually, it's more than making a chatbot. It's having a real robot > respond to a person based on linking utterances > (made by either the robot or the person) to the current context (milieu > entities and events). > > I think before you make your Worldcomp presentation it would behoove you > to read the *NEWCAT *and > *Computation of Language* books so that you can adequately articulate the > differences in your approach. > We seem to be talking past each other here. My presentation at Worldcomp need not compare with anything, most especially character-based methods that don't seem to even recognize what parsing applications need from a parser, let alone squarely addressing the how to provide what those applications need. There is SO much that these methods don't on first glance address. Each parsing method seems to need a champion, and you seem to be the resident champion for L-A grammar here. I know you want to just send me some hyperlinks and tell me to go away and read some books, but here on this forum we each learn our own particular areas, and defend against stones tossed by people defending nearby areas. I tossed a stone your way when I claimed blinding speed. You tossed a stone back when you explained that all that was needed to parse was to move about though L-A map of English grammar. I tossed the stone back, pointing out that losing the semantic elements (many of which are idioms that don't make much grammatical sense) throws the baby out with the bath water, because applications (other than machine translation) are only interested in semantics, not syntax. Dragging semantics out of a parse tree is a really BIG job, requiring the SAME tests as other parsing methods. Sure you produce a parse in a hurry by not doing the job of other parsers, but then doing that job loses the speed advantage. To illustrate some of the challenges, I took a large idiom dictionary and tried looking up idioms that I commonly use in everyday speech, and only found about half of them. So much for quality control. How does L-A deal with idioms? Once you have discarded the low-level semantic elements as part of putting words into parse trees, recognizing idioms could become quite difficult. Further. many idioms are ungrammatical. Are you planning to include idioms as part of the map of the language?!!! Anyway, I **DO** want to understand L-A enough to see if it is significant, or have you understand my method enough to be able to compare the two, so we can both see the relationships between these two VERY different things. Steve > > ------------------------------ > Date: Fri, 22 Mar 2013 15:30:59 -0700 > Subject: Re: [agi] 40 years of parsing NL... > > From: [email protected] > To: [email protected] > > PM, > > This guy is talking about a different approach for making a chatbot - > right? If so, he doesn't show any indication of knowing about present > chatbots. Present technology is to have a variety of sentence skeletons, > into which appropriate words and phrases are placed, which seems to work > quite well. > > I would think that promoting a technology would best be done with FREE > documents and other supporting material. I already have the 10,000 most > commonly used words in a file in order of frequency of use, if you or > anyone else wants a copy. > > I believe that my approach will be fast enough to keep up with the > Internet, and I haven't seen any other approach that promises such blinding > speed. In theory, all I need do is get the word out, and wait for folks at > Google, Yahoo, and Facebook to discover it, which is my present plan. > > I also plan to present this at the next WORLDCOMP conference. > > BTW, ***THANKS*** for holding my feet to the fire!!! I plan to adapt > these discussions into the paper I present at WORLDCOMP. > > Steve > =================== > On Fri, Mar 22, 2013 at 1:39 PM, Piaget Modeler <[email protected] > > wrote: > > Roland's next step: > > > http://www.amazon.com/Computational-Linguistics-Talking-Robots-Processing/dp/3642224318/ref=sr_1_1?ie=UTF8&qid=1363984424&sr=8-1&keywords=talking+robots+roland+hausser > > Computational Linguistics and Talking Robots: Processing Content in > Database Semantics > > Publication Date: September 14, 2011 | ISBN-10: 3642224318 | ISBN-13: > 978-3642224317 | Edition: 2011 > The practical task of building a talking robot requires a theory of how > natural language communication works. Conversely, the best way to > computationally verify a theory of natural language communication is to > demonstrate its functioning concretely in the form of a talking robot, the > epitome of human–machine communication. To build an actual robot requires > hardware that provides appropriate recognition and action interfaces, and > because such hardware is hard to develop the approach in this book is > theoretical: the author presents an artificial cognitive agent with > language as a software system called database semantics (DBS). Because a > theoretical approach does not have to deal with the technical difficulties > of hardware engineering there is no reason to simplify the system – instead > the software components of DBS aim at completeness of function and of data > coverage in word form recognition, syntactic–semantic interpretation and > inferencing, leaving the procedural implementation of elementary concepts > for later. In this book the author first examines the universals of natural > language and explains the Database Semantics approach. Then in Part I he > examines the following natural language communication issues: using > external surfaces; the cycle of natural language communication; memory > structure; autonomous control; and learning. In Part II he analyzes the > coding of content according to the aspects: semantic relations of > structure; simultaneous amalgamation of content; graph-theoretical > considerations; computing perspective in dialogue; and computing > perspective in text. The book ends with a concluding chapter, a > bibliography and an index. The book will be of value to researchers, > graduate students and engineers in the areas of artificial intelligence and > robotics, in particular those who deal with natural language processing. > > > For you, Steve, the next step is to write a book about your approach and > sell it for $100 a pop, or $75 for the e-book, > and do a book tour (if possible). > > Then gain some early adopters and market traction. > > The point is to make money WHILE promoting your idea. > > Cheers, > > ~PM > > ------------------------------ > Date: Fri, 22 Mar 2013 12:13:23 -0700 > Subject: [agi] 40 years of parsing NL... > From: [email protected] > To: [email protected] > > > 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> > <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10443978-6f4c28ac> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > > > > > -- > Full employment can be had with the stoke of a pen. Simply institute a six > hour workday. That will easily create enough new jobs to bring back full > employment. > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10443978-6f4c28ac> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Full employment can be had with the stoke of a pen. Simply institute a six hour workday. That will easily create enough new jobs to bring back full employment. ------------------------------------------- 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
