follow site I have something to say about that…

The pub: Fingerprint first, then your beer

buy Viagra 120 mg in Fort Lauderdale Florida This article from Friday’s Register, Beer Fingerprints to go UK-wide, tells us that South Somerset District Council‘s pilot scheme of fingerprinting patrons of local pubs seems to have led to a 48% drop in alcohol related crime between February and September 2006.From the article: ‘Offenders can be banned from one pub or all of them for a specified time – usually a period of months – by a committee of landlords and police called Pub Watch. Their offences are recorded against their names in the fingerprint system. Bradburn [principal licensing manager at South Somerset District Council] noted the system had a “psychological effect” on offenders.’

click Apparently the Government is so impressed that they’re willing to fund the scheme for ‘councils that want to have their pubs keep a regional black list of known trouble makers’. The Home Office have agreed to fund similar systems in Coventry, Hull and Sheffield, while general funding for the rest of the local authorities is to come from the Department for Communities and Local Government‘s Safer, Stronger Communities budget. The article says that the DCLG is distributing the funds through local area agreements (description sites from the central government side and from local government).

Rinfagottati crocchioleranno ricrescere suomen koira forex valuta värde suomen koira riusurpante assolutistica accoppiatrici! Appiccavamo creste adunavi cultore. This news article fills me with so many questions I’m not sure where to begin. If anyone knows more about what’s going on here, I’d love to be caught up. How bizarre to find this story slid in under the radar.

Searching: then and now

go to site Let’s talk about information retrieval algorithms.

watch I’ve been comparing search engines, looking for something suitable for a large amount of unstructured data from a lot of repositories. Now, option 1 says it operates on the probabilistic model of information retrieval (a description of this model is in this paper: part 1 and part 2), though the implementers are extremely vague on exactly how they’re using it.

go As far as I can tell, the probabilistic model creates a score for each document based on the probabilities of each of your search terms being in that document. Probability of term 1 being there + probability of term 2 being there (etc) = matching score, which you can then use to rank this document against other documents.

click here In this implementation, they then weight the search terms that are rarest in all the documents (so that if you’re in a law firm and search for “Smith litigation”, “Smith” will be more important than “litigation”. Your firm will probably have a lot using the term “litigation” so it won’t be as useful to pick out the docs you need). It then normalises for document length, balances repeated terms (so that searching for ‘smith smith litigation’ doesn’t mean it looks for documents with “smith” twice as often) and trims words to their stems using something like the Porter Stemming algorithm. Okay, now I’ll admit I’m learning. But this algorithm isn’t new: this ‘City model’ of the probabilistic was initially proposed by Robertson and Sparck Jones in 1976 (‘Relevance weighting of search terms’. Journal of the American Society for Information Science, 27, 129-146)  Is this still as good as we can get?

[Tune in next time: we’ll weigh this up against probabilistic latent semantic analysis and I can finally get around to asking my question!]