Using extension sets to aggregate partial rankings in a flexible setting

2016 ◽  
Vol 290 ◽  
pp. 208-223 ◽  
Author(s):  
Juan A. Aledo ◽  
Jose A. Gámez ◽  
David Molina
Keyword(s):  
Partial Rankings

Testing for a Trend in Partial Rankings

Statistics ◽  
1996 ◽  
Vol 27 (3-4) ◽  
pp. 339-343
Author(s):  
E. Stoimenova
Keyword(s):  
Partial Rankings

Computing distances between partial rankings

2009 ◽  
Vol 109 (4) ◽  
pp. 238-241 ◽  
Author(s):  
Mukul S. Bansal ◽  
David Fernández-Baca
Keyword(s):  
Partial Rankings

Amazigh Negative Verb Morphology

2015 ◽  
Vol 7 (2) ◽  
pp. 211-278
Author(s):  
Karim Bensoukas
Keyword(s):  
Proper Treatment ◽  
Verb Morphology ◽  
Partial Rankings

This paper provides an Optimality-theoretic account of Amazigh negative verb morphology. With very few exceptions, previous accounts focused more on particles than on the morphology proper. The infixation vs. final ablaut allomorphy and the correspondence between negative and affirmative paradigms have remained so far without proper treatment. Also, by assigning verbs to four paradigms, general descriptions of Amazigh overlook the neutralization of negative verb morphology in some three-paradigm Tashlhit dialects, treated in this paper as an identity avoidance effect, as well as the concomitant variation other Tashlhit dialects exhibit, which shows that Partial Rankings are at play. Also overlooked are the five paradigms of Tarifiyt, Iznassen and Figuig and the use of discontinuous negation due to the Jespersen’s cycle. The latter dialects not only show an extended negative morphology by overtly marking both the preterite and the intensive aorist but they also reinforce negation syntactically. Comparison with non-Moroccan dialects reveals various patterns pertaining to these aspects of negation.

Prototypes construction from partial rankings to characterize the attractiveness of companies in Belgium

2016 ◽  
Vol 42 ◽  
pp. 276-289 ◽  
Author(s):  
Gonzalo Nápoles ◽  
Zoumpolia Dikopoulou ◽  
Elpiniki Papageorgiou ◽  
Rafael Bello ◽  
Koen Vanhoof
Keyword(s):  
Partial Rankings

Comparing Partial Rankings

2006 ◽  
Vol 20 (3) ◽  
pp. 628-648 ◽  
Author(s):  
Ronald Fagin ◽  
Ravi Kumar ◽  
Mohammad Mahdian ◽  
D. Sivakumar ◽  
Erik Vee
Keyword(s):  
Partial Rankings

scholarly journals Comparison of Partially Ranked Lists

2017 ◽  
Vol 46 (3-4) ◽  
pp. 107-115
Author(s):  
Eugenia Stoimenova
Keyword(s):  
Ordered Categories ◽  
Partial Rankings

In this paper we introduce a measure of closeness of partial rankings based on a metric on permutations, and we analyze some of its properties. We consider two types of partial rankings: ranking the  k favorite items out of n and classification into several ordered categories.

scholarly journals Riffled Independence for Efficient Inference with Partial Rankings

2012 ◽  
Vol 44 ◽  
pp. 491-532 ◽  
Author(s):  
J. Huang ◽  
A. Kapoor ◽  
C. Guestrin
Keyword(s):  
Task Complexity ◽  
Exact Algorithms ◽  
Intelligence Community ◽  
Model Data ◽  
Large Space ◽  
Partial Ranking ◽  
Additional Challenge ◽  
Partial Rankings ◽  
Ranking Information ◽  
Mathematical Connection

Distributions over rankings are used to model data in a multitude of real world settings such as preference analysis and political elections. Modeling such distributions presents several computational challenges, however, due to the factorial size of the set of rankings over an item set. Some of these challenges are quite familiar to the artificial intelligence community, such as how to compactly represent a distribution over a combinatorially large space, and how to efficiently perform probabilistic inference with these representations. With respect to ranking, however, there is the additional challenge of what we refer to as human task complexity — users are rarely willing to provide a full ranking over a long list of candidates, instead often preferring to provide partial ranking information. Simultaneously addressing all of these challenges — i.e., designing a compactly representable model which is amenable to efficient inference and can be learned using partial ranking data — is a difficult task, but is necessary if we would like to scale to problems with nontrivial size. In this paper, we show that the recently proposed riffled independence assumptions cleanly and efficiently address each of the above challenges. In particular, we establish a tight mathematical connection between the concepts of riffled independence and of partial rankings. This correspondence not only allows us to then develop efficient and exact algorithms for performing inference tasks using riffled independence based represen- tations with partial rankings, but somewhat surprisingly, also shows that efficient inference is not possible for riffle independent models (in a certain sense) with observations which do not take the form of partial rankings. Finally, using our inference algorithm, we introduce the first method for learning riffled independence based models from partially ranked data.

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