similar to: GSOC 2011 : Weighting Schemes

Hi, Evaluation of pivoted normalization ("PPP") of tf-idf weighting scheme is also complete now. I have also evaluated the default tf-idf normalization ("ntn") and other normalizations combinations involving pivoted normalization in wdfn, idfn and wtn component as "Pxx", "xPx" and "xxP" normalization strings respectively to have a clear idea about

Hello guys.I just read up about tf-idf schemes and want to implement it in Xapian (with 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.)

Hello Everyone, I am a second year graduate student at IIIT-Bangalore and my interest is in the field of Information Retrieval. I have successfully compiled Xapian from source and have implemented some examples. While going through the project list Weighting Schemes project is the one I was looking to contribute to. So i went through the xapian-core/weight where most of the schemes are already

Hola, Acabo de calcular tf-idf y me surge una duda. ¿Habría un valor de idf o tf-idf que se considerara como umbral para establecer que una palabra es muy común o no? Los valores de idf en mis datos van entre 0 y 3.78 y los de tf-idf ente 0 y 0.07. Un saludo El Mar, 28 de Abril de 2020, 12:53, Carlos Ortega escribió: > Hola, > Yo de primeras los quitaría para qué otros topics aparecen.

Ah. If FIRE doesn't have something that can show this suitably, then > maybe Parth can advise on access to TREC, as I know he's used some of > them in the past. > ?I can say FIRE is also a reliable source but INEX/TREC are better. INEX can give you free access and TREC is not freely available. I had used INEX for xapian in the past and some details are here:

Hello list. I recently moved to an AD environment. I'm still keeping a samba servers to make my cups-managed printers available to windows users, rather than duplicating configuration with a Windows print service. But I'm facing two problems, probably due to the way we manage AD. First, all my host belong to a Unix-managed DNS domain (msr-inria.inria.fr), not to the windows-managed

df is a very large data frame with arrival estimates for many flights (DF$flightfact) at random times (df$PredTime). The error of the estimate is df$dt. My problem is that I want to know the prediction error at each minute before landing. This code works, but is very slow, and dominates everything. I tried using split(), but that rapidly ate up my 12 GB of memory. So, is there a better R way of

Hi, I''m confused about managing field boosting ... I have set the :boost for the :name field in my docs to 10, via :boost => 10 Then I performed a search for ''keith'' over all fields via with *:(keith*), expecting a doc with Keith in the :name field to come out on top. But another doc with Keith mentioned in other fields (:comments, :address) scored higher. I

Hello guys.I have sent a pull request for the code and tests of the Tf-Idf weighting scheme. Please do let me know if any changes are required.Meanwhile,Ill begin working on implementing normalizations which require additional statistics and on the DFR schemes. https://github.com/xapian/xapian/pull/6 On Tue, Feb 26, 2013 at 5:30 PM, <xapian-devel-request at lists.xapian.org>wrote: >

Dear R users,I'm a new user of R. I'm trying to do a LM test an there is this type of error: Error in t(mX) %*% mX : requires numeric/complex matrix/vector arguments. To be clear I write down the code in which mY ( 126,1 ) mX (126,1) mZ(126,1) are matrix. LMTEST <- function(mY, mX, mZ)#mY, mX, mZ must be matrices!#returns the LM test statistic and the degree of freedom{iT =

Hi I have a 3 x 2 contingency table: 10 20 30 40 50 60 I want to update the frequencies to new marginal totals: 100 130 40 80 110 I want to use the ipf (iterative proportional fitting) function which is apparently in the cat package. Can somebody please advice me how to input this data and invoke ipf in R to obtain an updated contingency table? Thanks. By the way I am quite new to R. -- Dr

Hello, I'm Shivang Bansal, a 3rd year Computer Science Engineering undergraduate at Institute of Engineering & Technology in Lucknow, India. This mail is an expression of my interest for Google Summer of Code program of this year. I want to apologize for getting in so late. Actually I would have contacted earlier, but sudden demise of my Grandfather disabled me in doing so. I am

Hello all, I am very sorry I did not include xapian-devel mailing list in my previous mail. Thanks for responding my mail. Mohd Azeem NIT UK ________________________________ From: Olly Betts <olly at survex.com> To: Mohd Azeem <azeem201001 at yahoo.in> Cc: Parth Gupta <parthg.88 at gmail.com> Sent: Saturday, 31 March 2012 11:40 AM Subject: Re: GSoC, Xapian Project Weighting

Tf-idf is most used used weighting scheme is easy to understand and has been used in other frameworks like lucene and many other places. okapi bm25(implemented in xapian) is theoretically better/improved measure than tf-idf and i am looking into various other weighting scheme which are there in xapian or can be implemented like TF-ICF(term frequecy inverse corpus frequency),TF-RF(term

Hello, I am the author of the Bonk audio compression program... i've just been looking at your comparrison table, and i noticed bonk gets marginally better compression than Flac on some files (actually i was rather surprised to see bonk on the list at all, it's not exactly high profile :-) ). Bonk in lossless mode is a pretty naive implementation of a predictive coder, so the main

Hi all, I'm hoping someone might help with a query about conditionally applying formulas to a dataframe. In essence I have 3 lookup tables (Table A, B & C) and a dataframe with a variable Type.Code, which identifies the Lookup Table to which each record belongs. The lookup tables reference different sensor types for which I need apply a different formula to values in Column3 in each row

Hello, Thanks James for the reply. That cleared a few things out. Apologies for replying late because of exams going on. I was going through the previous clustering API to understand how it worked and it seems like the the approach for construction of the termlists which are used for distance metrics use TF-IDF weighting with cosine similarity, which is very similar to the approach I would need

I'm having trouble using some fairly simple code to change the entries in a vector (x - the numbers 0-5) according to a simple transition matrix that I've called p.dry. the error message I get is "no finite arguments to min; returning Inf" Any suggestions as to where I'm going wrong greatly appreciated. Sorry the message is lengthy! cheers Nick The code is

Hi, all: This is the basic implementation of tf-idf scheme (basic scheme used in SMART) that can be used in the Xapian. It might still need some futher revision, but I believe it works anyway.:) I modified the weight.h to define a subclass Tf_idfWeight and add a new file tf_idf.cc in ../weight in the repo, to implement Tf_idfWeight. Here is the git diff patch: https://gist.github.com/2422049

Hello guys :) I have sent a pull request for the Tf-Idf Weighting scheme incorporating as many normalizations as I could with the help of statistics currently available from Xapian::Weight . Please let me know what you'll think about it. I used the weighting scheme in a simple searcher and it did a fine job with it. I have no experience with writing tests for features like this.Please give me