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في ٢٤/٠٣/٢٠١٣، الساعة ١٠:٢٠ م، كتب Steve Richfield <[email protected]>: > Fatmah, > > On Sun, Mar 24, 2013 at 6:22 AM, Fatmah <[email protected]> wrote: >> we developed some units > > What did they do? > > Steve >> >> From: Steve Richfield <[email protected]> >> To: AGI <[email protected]> >> Sent: Saturday, March 23, 2013 6:57 PM >> >> Subject: Re: [agi] 40 years of parsing NL... >> >> PM (and Logan), >> >> You said in a previous posting that you have experience with L-A. What have >> you (or others) done with it? >> >> I ask because once you sidestep semantic units, it seems to me like you >> have thrown the baby out with the bathwater, at least for the usual >> applications needing some degree of "understanding". Maybe I just haven't >> noticed a good application that doesn't need semantic units, or I haven't >> understood a good way to live without them. Sure you can "parse" while >> ignoring them, but then of what use is the resulting parse?!!! >> >> Idioms (of which there are thousands) are a sort of ill-behaved semantic >> unit. How do you handle idioms while sidestepping semantic units? >> >> Logan: Have you been following this discussion? RADP is close enough to what >> I am planning to have the same semantic unit needs. Can you help make sense >> of this? >> >> What (if anything) am I missing here? >> >> Steve >> ================= >> On Fri, Mar 22, 2013 at 7:08 PM, Steve Richfield <[email protected]> >> wrote: >> 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 | Modify Your Subscription >> AGI | Archives | Modify Your Subscription >> >> >> >> -- >> 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 | Modify Your Subscription >> AGI | Archives | Modify Your Subscription >> >> >> >> -- >> 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. >> >> >> >> >> -- >> 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 | Modify Your Subscription >> >> >> AGI | Archives | Modify Your Subscription > > > > -- > Full employment can be had with the stoke of a pen. Simply institute a six > hour workday. 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