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...some frequently used normalizations) as it will also give me a good hang of implementing a weighting scheme before I start working on implementing DFR schemes. I read the following as references and I think Ive understood it well and can write the hack :- 1.) http://nlp.stanford.edu/IR-book/html/htmledition/document-and-query-weighting-schemes-1.html 2.) http://classes.seattleu.edu/computer_science/csse470/Madani/ABCs.html 3.) http://en.wikipedia.org/wiki/Tf%E2%80%93idf The basic philosophy is that rare terms (terms which occur in a few documents) should be able to give a higher weight to the doc...

...uery and tries to pull potential document of all context rather than explicitly assuming a context. Diversification can be done by generating different rank list for different context or adding document from different context in a single rank list. Resources: http://nlp.stanford.edu/IR-book/html/htmledition/ponte-and-crofts-experiments-1.html http://dl.acm.org/citation.cfm?id=291008 http://goo.gl/klqYy http://dl.acm.org/citation.cfm?id=1860709 I have compiled and installed xapian and tried playing with xapian in past few days.I have few queries regarding xapian :- 1. xapain supports relevance fe...

On Sun, Mar 6, 2016 at 7:17 AM, James Aylett <james-xapian at tartarus.org> wrote: > On Sat, Mar 05, 2016 at 10:58:43PM +0530, Richhiey Thomas wrote: > > K-Means or something related certainly seems like a viable approach, > so what you'll need to do is to come up with a proposal of how you'd > implement this in Xapian (either with reference to the previous work, >

...;> We had a discussion a month ago I think about the Spell Correction >> algorithm which was used in CLang, notably for auto-completion, based on the >> Levenstein distance. >> >> It turns out I just came upon this link today: >> http://nlp.stanford.edu/IR-book/html/htmledition/k-gram-indexes-for-spelling-correction-1.html >> >> The idea is to use bigrams (2-letters parts of a word) to build an index >> of the form (bigram > all words containing this bigram), then use this index >> to retrieve all the words with enough bigrams in common with...

...ys, but we're generally trying to produce 'good' clusters as a human with knowledge of the subject area would create. There's some discussion of how you might evaluate this in Introduction to Information Retrieval, section 16.3 p356 (or online at http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html). It's perhaps worth pointing out that Hearst (2009, p200) suggests that monothetic clusters 'may be easier for users to understand', although it doesn't cite any specific work to back up this claim. But that may argue that a K-means based approac...

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