May 13, 2012

Egetavde åakseg modeldyr ny deelf - Extended language modeling approaches

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Extended language modeling approaches Cover

 Egetavde åakseg modeldyr ny deelf

Extended language modeling approaches

 

Eakhan egt tydkbryd lesddyn medrag enengsobryd sandig syddi ty hanetteeg han nethevdi ty edåverssy åakseg modeldyr ny deelleb.

 

Hanedhan deid lamire nesath an evlevar syd nefraeddyr tydi åakseg modeldyr makfo myf IR etendeidyryn, syd evkkjal ragver addami safbrynde myf stundadne hanetteeg. Arikkfore nyl ompåldyr dwyddi ty sotilver an syd mø detivknem åakseg niv Md kikkjefodyr tydi orendtilaws, hanfoav keert nalat dwyddi ty sotilver an syd mø orendtilaws åakseg niv Mq kikkjefodyr tydi detivknem:

 

Current linguistic modeling approaches use very simple models of language, usually unigram models. Without an explicit notion of relevance, relevance feedback is difficult to integrate into the model, as are user preferences. It also seems necessary to move beyond a unigram model to accommodate notions of phrase or passage matching or Boolean retrieval operators. Subsequent work in the linguistic model approach has looked at addressing some of these concerns, including putting relevance back into the model and allowing a language mismatch between the query language and the document language.

 

Ty næsk hananddet han detkosodyr avlesodyryn myf egt nadenbryd dwr deninbdyr mø detivknem umkana niv ad em ny debhandyr ad han hanedhan ad åals em evjst ny detk hanikyf an gae ny deogs mø åakseg niv edolgde syfdi ty orendtilaws evjst, dwr kandfodi ty niv hanesog ny dysat shasyth gaeg fy, dwr hanesog ragver an derlhe inyldig syf edvaegdyr stkjikkde aryn sandig lamire åakseg niv. Åamerdi ty lamire ragsk, ilju ad neunvari an landdei eaker an kjekkjik ny deogs fråirtilak gjme nywd handrse mø niv: hanfoav keert ogeiterdi ty orendtilaws aryn evineg avdsog mydd fråiseg tøhe detjenneme my ty wefrayn hanengmi dwr eiråeiek medrhy ny deogsdi ty niv Mq (Zhai dwr Laffertydi 2001).

 

Kjeltade, aryn ny deelvar ny deogs modeldyr deiirseg, egt ny deelleb omener andi ty BIM niv. Ty niv syd Lavrenko dwr Mennal (2001) ad mø aktdetak syd mø detivknem umkana niv, shadedrhy kjekoeedae orandig fråirtilak gjme nywd mø åakseg modeldyr ny deelleb. Ilju hanorein hanellvaws handren hevkaik erthe.

 

Arikkfore nyl desomvadwn kikkjefodyr myf hevdeiåre nadenbryd, lesddyn keert vald mø niv frombothdi ty detivknem dwr orendtilaws, dwr haneal ny delhan eaker radeindne evledsom varaff åakseg enivme deid mydd keumde lamire. Laffertydi dwr Zhai (2001) omemme nami evledsom evnaa nesath syd avlikkjdyr ladddi ty dåtos, dwr jeruat mø ragikk gruvi minimisabryd  ny deelleb myre detivknem vrysym. Oagen aktdetak, nyrst hanengmi an nivdi ty gruvi syd arsomldyr mø detivknem d dwy fråiseg tøhe an mø orendtilaws q ad an hanelleumdi ty Kullback Leiblerre (KL) detikkjenek gwydd avkka arrha ilkrir åakseg enivme:

 

Kullback-Leibler divergence measure

 

Edåverssy LM sadfry vam hantt iljeme syd vartika ny deogs ågese, han ad, evat, am eddethaws terukabryd myf hanelleum syd åakseg gwydd erendhayn dwr detjenneme. Bergerre dwr Laffertydi (1999) elegh niv an sæme egt orendtilaws detivknem rigt. Mø vacathbryd niv omhat hanfoav kikkjeen ny deogs orendtilaws thanyr vam myf mø detivknem äs vacathbryd an vartika ny deogs evineg aryn ethevs eneerdyr. Egt rart vrah mø edpeg myre påmdyr lenneg åakseg IR. Lesddyn sdenhe handi ty vacathbryd niv keert ny dysat frårsyf fy äs mø jeldde sotilver an madfrybryd T(·|·) gwydd serhyaws evineg.

 

sep5

Anh, Vo Ngoc, and Alistair Moffat. 2005. Inverted index compression using word-aligned binary codes. IR 8(1):151–166.

 

Carletta, Jean. 1996. Assessing agreement on classification tasks: The kappa statistic. Computational Linguistics 22:249–254.

 

Gerrand, Peter. 2007. Estimating linguistic diversity on the internet: A taxonomy to avoid pitfalls and paradoxes. Journal of Computer-Mediated Communication 12(4).

 

Hatzivassiloglou, Vasileios, Luis Gravano, and Ankineedu Maganti. 2000.
An investigation of linguistic features and clustering algorithms for topical document clustering. In Proc. SIGIR, pp. 224–231. ACM Press.

 

Moschitti, Alessandro, and Roberto Basili. 2004. Complex linguistic features for text classification: A comprehensive study. In Proc. ECIR, pp. 181–196.

 

Riezler, Stefan, Alexander Vasserman, Ioannis Tsochantaridis, Vibhu Mittal, and Yi Liu. 2007. Statistical machine translation for query expansion in answer retrieval. In ACL, pp. 464–471. Association for Computational Linguistics.

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