I asked before, whether or not someone might be interested in improving the kind N network detection algorithm. -- Well, I figured, I "shot first, asked then", figuratively, implemented the approach before asking Google about the issue. Hm. Foolish.
But, as I grasped the idea now, that's a chance for further improvement of the detection approach. However, I want to get that baby implemented once in complete before I go into any source improving. Hence, I keep the offer: If you're interested in diving into MOM, the source, want to improve it, its source, or especially the kind N network detection, please let me know. I'd be curiously to hear from you.
Updates: none so far
Showing posts with label search results. Show all posts
Showing posts with label search results. Show all posts
Friday, August 10, 2007
Friday, July 13, 2007
Positive hits in the "content representation" search results
Correct hits on the "content representation" term Google search (in opposite to any such hits that contained "content <something else but whitespace only, such as punctuation> representation"):
The above results I picked from pages 79 and 78 only -- and already learned a lession: It might make more sense to apply some kind of clustering here instead of walking through the list manually. Even the intellectual check whether there is anything in between of "content" and "representation" -- to filter out false hits --, can be done by software.
I'd like to learn the most-often used terms (besides of "content representation"), and, by help of that clustering/visualization, I want to get the chance to ignore obvious false hits.
That demands for using -- get hands on -- the Google API.
Updates: none so far
- Mining Web Documents for Unintended Information Revelation
- LHNCBC: Mansur Project - Anatomical Text to Images
- Data processing: presentation processing of document patents new
- Philippe Jost - LTS2
- nformation Sciences Institute - Research
- [PDF] Music notation/representation requirements for the protection of ...
- [PDF] Microsoft PowerPoint - Research_GET_STIC ASIE.ppt
- Informed Content Delivery Across Adaptive Overlay Networks
- Digital Television Applications
- [PDF] Abstract Model, 70–76 Accessibility characteristics, 262 Act, 217 ...
- [PS] Selecting Task-Relevant Sources for Just-in-Time Retrieval
- [Cc-uk] FW: IEE Events] The 2nd European Workshop on the ...
- [PDF] T R D R
- XML Pitstop : Largest Source of XML Examples on the Web
- ...
The above results I picked from pages 79 and 78 only -- and already learned a lession: It might make more sense to apply some kind of clustering here instead of walking through the list manually. Even the intellectual check whether there is anything in between of "content" and "representation" -- to filter out false hits --, can be done by software.
I'd like to learn the most-often used terms (besides of "content representation"), and, by help of that clustering/visualization, I want to get the chance to ignore obvious false hits.
That demands for using -- get hands on -- the Google API.
Updates: none so far
Sunday, July 01, 2007
other models of meaning
Maybe worth a skim: Search results on 'Model of Meaning'. ('Content Representation with a Twist' didn't find anything so far, neither on Google, nor on Yahoo. Although Yahoo's crawler visited the MOM development project page over at gna.org.)
Updates: none so far
Updates: none so far
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