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|    comp.ai.fuzzy    |    Fuzzy logic... all warm and fuzzy-like    |    1,275 messages    |
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|    Message 93 of 1,275    |
|    =?ISO-8859-1?Q?Thomas_Kr=E4mer?= to All    |
|    nn in information retrieval    |
|    03 Dec 03 17:04:14    |
      XPost: comp.ai.neural-nets       From: kraemert@smail.uni-koeln.de              Hello fuzzy / neural network implementers                     i am building an extension for lucene, the search engine project within       apache , that integrates the user´s       query history into the retrieval process.              one idea is to implement a neural network, that adapts to the users       information need by reweighting search terms.              furthermore there are 2 different levels of text representation, one by       a large collection of metadata and pure fulltext search, and a second       based on the same metadata AND the entire document.       the latter is permanently changed in order to obtain a representation       closer to what a single user considers relevant.              first, the application performs a full-text search on a metadata corpus       using lucene.              the user then selects the apparently most interesting documents (by its       dc metadata). this second user-input should be used to adapt the neural       network weights.                     until now, i am not entirely decided which neural network api i should       use. joone, at a first glance, seems to be quite user friendly.              question 1 :              does an easy to use "term-document-matrix builder" exist, that       constructs t-dimensional matrix containing all the terms in the corpus       mapped to the documents where they occured?       it would be helpful if it were easily possible to compute the       tf-idf-measure instead of the absolute term-occurence values (integers).              i thought of a simple 2-layered nn, in wich one of the layers represents       documents and the second one all the terms in the corpus (cf. google:       cosimir)              so, once the users decides to see the full text of several docs       refenrenced in metadata retrieved by a full text search according to teh       users initial query terms, it must be possible to increase the       activation of those neurons that represent the documents selected for       full retrieval.              quetion 2:              do you know whether there is a more appropriate (open source) software       to build such a nn?              i am a real newbie to ai and appreciate your opinion.                     thanks for any hint!                     thomas krämer              --- SoupGate-Win32 v1.05        * Origin: you cannot sedate... all the things you hate (1:229/2)    |
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