I will make 3 suggestions. All are out of copyright, well known, uncontroversial, and still taught in schools (At least in the US)
1. Robinson Crusoe - Daniel Defoe http://www.gutenberg.org/ebooks/521 2. Great Expectations - Charles Dickens http://www.gutenberg.org/ebooks/1400 3. The Time Machine - H.G. Wells http://www.gutenberg.org/ebooks/35 Ian On Sat, Aug 24, 2013 at 10:24 AM, Francisco Webber <[email protected]> wrote: > For those who don't want to use the API and for evaluation purposes, I > would propose that we choose some reference text and I convert it into a > sequence of SDRs. This file could be used for training. > I would also generate a list of all words contained in the text, together > with their SDRs to be used as conversion table. > As a simple test measure we could feed a sequence of SDRs into a trained > network and see if the HTM makes the right prediction about the following > word(s). > The last file to produce for a complete framework would be a list of lets > say 100 word sequences with their correct continuation. > The word sequences could be for example the beginnings of phrases with > more than n words (n being the number of steps ahead that the CLA can > predict ahead) > This could be the beginning of a measuring set-up that allows to compare > different CLA-implementation flavors. > > Any suggestions for a text to choose? > > Francisco > > On 24.08.2013, at 17:12, Matthew Taylor wrote: > > Very cool, Francisco. Here is where you can get cept API credentials: > https://cept.3scale.net/signup > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > > On Fri, Aug 23, 2013 at 5:07 PM, Francisco Webber <[email protected]>wrote: > >> Just a short post scriptum: >> >> The public version of our API doesn't actually contain the generic >> conversion function. But if people from the HTM community want to >> experiment just click the "Request for Beta-Program" button and I will >> upgrade your accounts manually. >> >> Francisco >> >> On 24.08.2013, at 01:59, Francisco Webber wrote: >> >> > Jeff, >> > I thought about this already. >> > We have a REST API where you can send a word in and get the SDR back, >> and vice versa. >> > I invite all who want to experiment to try it out. >> > You just need to get credentials at our website: www.cept.at. >> > >> > In mid-term it would be cool to create some sort of evaluation set, >> that could be used to measure progress while improving the CLA. >> > >> > We are continuously improving our Retina but the version that is >> currently online works pretty well already. >> > >> > I hope that will help >> > >> > Francisco >> > >> > On 24.08.2013, at 01:46, Jeff Hawkins wrote: >> > >> >> Francisco, >> >> Your work is very cool. Do you think it would be possible to make >> available >> >> your word SDRs (or a sufficient subset of them) for experimentation? I >> >> imagine there would be interested in the NuPIC community in training a >> CLA >> >> on text using your word SDRs. You might get some useful results more >> >> quickly. You could do this under a research only license or something >> like >> >> that. >> >> Jeff >> >> >> >> -----Original Message----- >> >> From: nupic [mailto:[email protected]] On Behalf Of >> Francisco >> >> Webber >> >> Sent: Wednesday, August 21, 2013 1:01 PM >> >> To: NuPIC general mailing list. >> >> Subject: Re: [nupic-dev] HTM in Natural Language Processing >> >> >> >> Hello, >> >> I am one of the founders of CEPT Systems and lead researcher of our >> retina >> >> algorithm. >> >> >> >> We have developed a method to represent words by a bitmap pattern >> capturing >> >> most of its "lexical semantics". (A text sensor) Our word-SDRs fulfill >> all >> >> the requirements for "good" HTM input data. >> >> >> >> - Words with similar meaning "look" similar >> >> - If you drop random bits in the representation the semantics remain >> intact >> >> - Only a small number (up to 5%) of bits are set in a word-SDR >> >> - Every bit in the representation corresponds to a specific semantic >> feature >> >> of the language used >> >> - The Retina (sensory organ for a HTM) can be trained on any language >> >> - The retina training process is fully unsupervised. >> >> >> >> We have found out that the word-SDR by itself (without using any HTM >> yet) >> >> can improve many NLP problems that are only poorly solved using the >> >> traditional statistic approaches. >> >> We use the SDRs to: >> >> - Create fingerprints of text documents which allows us to compare >> them for >> >> semantic similarity using simple (euclidian) similarity measures >> >> - We can automatically detect polysemy and disambiguate multiple >> meanings. >> >> - We can characterize any text with context terms for automatic >> >> search-engine query-expansion . >> >> >> >> We hope to successfully link-up our Retina to an HTM network to go >> beyond >> >> lexical semantics into the field of "grammatical semantics". >> >> This would hopefully lead to improved abstracting-, conversation-, >> question >> >> answering- and translation- systems.. >> >> >> >> Our correct web address is www.cept.at (no kangaroos in Vienna ;-) >> >> >> >> I am interested in any form of cooperation to apply HTM technology to >> text. >> >> >> >> Francisco >> >> >> >> On 21.08.2013, at 20:16, Christian Cleber Masdeval Braz wrote: >> >> >> >>> >> >>> Hello. >> >>> >> >>> As many of you here i am prety new in HTM technology. >> >>> >> >>> I am a researcher in Brazil and I am going to start my Phd program >> soon. >> >> My field of interest is NLP and the extraction of knowledge from text. >> I am >> >> thinking to use the ideas behind the Memory Prediction Framework to >> >> investigate semantic information retrieval from the Web, and answer >> >> questions in natural language. I intend to use the HTM implementation >> as >> >> base to do this. >> >>> >> >>> I apreciate a lot if someone could answer some questions: >> >>> >> >>> - Are there some researches related to HTM and NLP? Could indicate >> them? >> >>> >> >>> - Is HTM proper to address this problem? Could it learn, without >> >> supervision, the grammar of a language or just help in some aspects as >> Named >> >> Entity Recognition? >> >>> >> >>> >> >>> >> >>> Regards, >> >>> >> >>> Christian >> >>> >> >>> >> >>> _______________________________________________ >> >>> nupic mailing list >> >>> [email protected] >> >>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> >> >> >> >> _______________________________________________ >> >> nupic mailing list >> >> [email protected] >> >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> >> >> >> >> _______________________________________________ >> >> nupic mailing list >> >> [email protected] >> >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> > >> > >> > _______________________________________________ >> > nupic mailing list >> > [email protected] >> > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > > > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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