similar to: GSoc 2017 Introduction(Weighting Schemes)

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 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.)

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

Hi All, I am trying to compile xapian-core from the git source tree and after running the autoconf successfully while running configure getting the following error configure: creating ./config.status config.status: error: cannot find input file: `Makefile.in' can some one help me to solve this problem. Thanks & Regards Prakash

Hi, I have added support for Piv normalization in Tf-Idf weighting scheme as a intermediate step to implementing the support for Piv+ normalization. All tests pass. But I'm running into some issues with Piv+ normalization. In the Piv+ formula , there are two parameters (s and delta) that control the weight assigned. I think the way I'm serialising and unserialising these parameters has

On Jul 2, 2013, at 5:16 PM, Kjell Claesson wrote: >> I hope other people can also test this. >> >> Regards >> Alf Hogemark > > Due to some crashes on the disks and lack of time I don't have the git on the > pc now. But maybe some other can commit this. I'd like to ask people to specifically try the bcmxcp branch on GitHub - it includes some changes

Hi All, I'm Sumith, a postgraduate student in Monash university. I'm working in the area of Text weighting schemes and Text Mining. When I'm going through the GSOC project list, I felt interested in the 'Weighting Schemes' project. At the moment, I have worked with different weighting schemes as TF-IDF and would love to join and contribute with my ideas in this project.

Hi Inspired by the changes by Prachi Gandhi to use the command map from the UPS in the bcmxcp driver, I think the map should be used, if available, to control all commands the driver adds using "dstate_addcmd". The following patch is an attempt at doing so. The patch is against the bcmxcp branch. Since my UPS also supports : #define PW_UPS_ON (unsigned char)0x89 /* UPS on

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.

> Two of those are compile errors, suggesting you aren't pulling in the > right header file (it's in common/serialise-double.h I believe). Thanks, fixed those errors. > I can't tell for sure without seeing the diff. You may mean just > `ptr++`? But it could be something else, depending on what you're > trying to do. I'm trying to unserialise normalization

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''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

Buenos días, Estoy realizando un análisis de topic models con el método LDA. En principio, he quitado del análisis las palabras "stopwords" universales. A la hora de ver los topics y sus palabras más frecuentes encuentro que son muy similares y hay palabras que aparecen en todos los topics. Los textos que estoy analizando son opiniones de consumidores sobre una categoría concreta de

Hello, I have a Samba PDC (3.x) running in a OpenSUSE 10.2 system. The authentication backend is Open LDAP. I want to create a group share (WTCCC) which should be accessible to a group of users (belonging to a group called WTCCC). The users' possess this group as their secondary group (NOT primary). And the share folder would have its gid bit set, so all the writes to the folder

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 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: >

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

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

Hi Dan, Thanks for the reply. I got the labels for each outgoing edge (at the source node's 'structure' field) working. Is there a way to find out the outgoing edge number from EdgeIter. (Basically the Node in my graph has a a bunch of outgoing edges, so that I can just index into that collection within the node to get the appropriate edges' attributes). regards, Prakash On Tue,

Hi Prakash, I don't know of an easy way to do this, other than to use random-access iterators so you can compute the distance between the edge and the beginning of the list of edges. Dan On Sep 16, 2008, at 2:58 PM, Prakash Prabhu wrote: > Hi Dan, > > Thanks for the reply. I got the labels for each outgoing edge (at the > source node's 'structure' field) working. Is