162 research outputs found
Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding
Modern expert nding algorithms are developed under the
assumption that all possible expertise evidence for a person
is concentrated in a company that currently employs the
person. The evidence that can be acquired outside of an
enterprise is traditionally unnoticed. At the same time, the
Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only
Modeling Documents as Mixtures of Persons for Expert Finding
In this paper we address the problem of searching for knowledgeable
persons within the enterprise, known as the expert finding (or
expert search) task. We present a probabilistic algorithm using the assumption
that terms in documents are produced by people who are mentioned
in them.We represent documents retrieved to a query as mixtures
of candidate experts language models. Two methods of personal language
models extraction are proposed, as well as the way of combining
them with other evidences of expertise. Experiments conducted with the
TREC Enterprise collection demonstrate the superiority of our approach
in comparison with the best one among existing solutions
Using the Global Web as an Expertise Evidence Source
This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track. The presented study demonstrates the predicting potential of the expertise evidence that can be found outside of
the organization. We discovered that combining the ranking built solely on the Enterprise data with the Global Web
based ranking may produce significant increases in performance. However, our main goal was to explore whether
this result can be further improved by using various quality measures to distinguish among web result items. While,
indeed, it was beneficial to use some of these measures, especially those measuring relevance of URL strings and titles,
it stayed unclear whether they are decisively important
Entity Ranking on Graphs: Studies on Expert Finding
Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models
Finding Influential Training Samples for Gradient Boosted Decision Trees
We address the problem of finding influential training samples for a
particular case of tree ensemble-based models, e.g., Random Forest (RF) or
Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this
problem is studying how the model's predictions change upon leave-one-out
retraining, leaving out each individual training sample. Recent work has shown
that, for parametric models, this analysis can be conducted in a
computationally efficient way. We propose several ways of extending this
framework to non-parametric GBDT ensembles under the assumption that tree
structures remain fixed. Furthermore, we introduce a general scheme of
obtaining further approximations to our method that balance the trade-off
between performance and computational complexity. We evaluate our approaches on
various experimental setups and use-case scenarios and demonstrate both the
quality of our approach to finding influential training samples in comparison
to the baselines and its computational efficiency.Comment: Added the "Acknowledgements" sectio
On homogenization of electromagnetic crystals formed by uniaxial resonant scatterers
Dispersion properties of electromagnetic crystals formed by small uniaxial
resonant scatterers (magnetic or electric) are studied using the local field
approach. The goal of the study is to determine the conditions under which the
homogenization of such crystals can be made. Therefore the consideration is
limited by the frequency region where the wavelength in the host medium is
larger than the lattice periods. It is demonstrated that together with known
restriction for the homogenization related with the large values of the
material parameters there is an additional restriction related with their small
absolute values. From the other hand, the homogenization becomes allowed in
both cases of large and small material parameters for special directions of
propagation. Two unusual effects inherent to the crystals under consideration
are revealed: flat isofrequency contour which allows subwavelength imaging
using canalization regime and birefringence of extraordinary modes which can be
used for beam splitting.Comment: 16 pages, 12 figures, submitted to PR
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