Ian, I also thought about something from the Gutenberg repository. But I think we should start with something from the Kids Shelf.
There are several reasons in my opinion: - We start experimentation with a full bag of unknown parameters, so keeping the test material simple would allow us to detect the important ones sooner. And it is quite some work to create a reliable evaluation framework, so the size of the data set makes a difference. - Keeping the text simple and short reduces substantially the overall vocabulary. If we want people to also evaluate offline, matching fingerprints can become a lengthy process without an efficient similarity engine. - Another reason is the fact that we don't know how much a given set of columns (like the 2048 typically used) can absorb information. In other words: what is the optimal ratio between a first layer of a text-HTM and the amount of text. - Lastly I believe that the sequence in which text is presented to the CLA is of importance. After all when humans learn information by reading, they also start from simple to complex language. The amount of new vocabulary during training, should be relatively stable (the actual amount would probably be linked to the ratio of my previous argument) So we should build continuously more complex training data sets, finally ending up with "true" books like the ones you listed. To start I would suggest something like: A Primary Reader: Old-time Stories, Fairy Tales and Myths Retold by Children http://www.gutenberg.org/ebooks/7841 But there might still be better ones… Francisco On 25.08.2013, at 23:05, Ian Danforth wrote: > 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 > > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
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