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 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
