4,973 research outputs found
Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors
Statistical significance testing is widely accepted as a means to assess how
well a difference in effectiveness reflects an actual difference between
systems, as opposed to random noise because of the selection of topics.
According to recent surveys on SIGIR, CIKM, ECIR and TOIS papers, the t-test is
the most popular choice among IR researchers. However, previous work has
suggested computer intensive tests like the bootstrap or the permutation test,
based mainly on theoretical arguments. On empirical grounds, others have
suggested non-parametric alternatives such as the Wilcoxon test. Indeed, the
question of which tests we should use has accompanied IR and related fields for
decades now. Previous theoretical studies on this matter were limited in that
we know that test assumptions are not met in IR experiments, and empirical
studies were limited in that we do not have the necessary control over the null
hypotheses to compute actual Type I and Type II error rates under realistic
conditions. Therefore, not only is it unclear which test to use, but also how
much trust we should put in them. In contrast to past studies, in this paper we
employ a recent simulation methodology from TREC data to go around these
limitations. Our study comprises over 500 million p-values computed for a range
of tests, systems, effectiveness measures, topic set sizes and effect sizes,
and for both the 2-tail and 1-tail cases. Having such a large supply of IR
evaluation data with full knowledge of the null hypotheses, we are finally in a
position to evaluate how well statistical significance tests really behave with
IR data, and make sound recommendations for practitioners.Comment: 10 pages, 6 figures, SIGIR 201
Compare statistical significance tests for information retrieval evaluation
Preprint of our Journal of the Association for Information Science and Technology (JASIST) paper[Abstract] Statistical significance tests can provide evidence that the observed difference in performance between two methods is not due to chance. In Information Retrieval, some studies have examined the validity and suitability of such tests for comparing search systems.We argue here that current methods for assessing the reliability of statistical tests suffer from some methodological weaknesses, and we propose a novel way to study significance tests for retrieval evaluation. Using Score Distributions, we model the output of multiple search systems, produce simulated search results from such models, and compare them using various significance tests. A key strength of this approach is that we assess statistical tests under perfect knowledge about the truth or falseness of the null hypothesis. This new method for studying the power of significance tests in Information Retrieval evaluation is formal and innovative. Following this type of analysis, we found that both the sign test and Wilcoxon signed test have more power than the permutation test and the t-test. The sign test and Wilcoxon signed test also have a good behavior in terms of type I errors. The bootstrap test shows few type I errors, but it has less power than the other methods tested.Ministerio de Econom´ıa y Competitividad; TIN2015-64282-RXunta de Galicia; GPC 2016/035Xunta de Galicia; ED431G/01Xunta de Galicia; ED431G/0
Human evaluation of Kea, an automatic keyphrasing system.
This paper describes an evaluation of the Kea automatic keyphrase extraction algorithm. Tools that automatically identify keyphrases are desirable because document keyphrases have numerous applications in digital library systems, but are costly and time consuming to manually assign. Keyphrase extraction algorithms are usually evaluated by comparison to author-specified keywords, but this methodology has several well-known shortcomings. The results presented in this paper are based on subjective evaluations of the quality and appropriateness of keyphrases by human assessors, and make a number of contributions. First, they validate previous evaluations of Kea that rely on author keywords. Second, they show Kea's performance is comparable to that of similar systems that have been evaluated by human assessors. Finally, they justify the use of author keyphrases as a performance metric by showing that authors generally choose good keywords
Coherent Keyphrase Extraction via Web Mining
Keyphrases are useful for a variety of purposes, including summarizing,
indexing, labeling, categorizing, clustering, highlighting, browsing, and
searching. The task of automatic keyphrase extraction is to select keyphrases
from within the text of a given document. Automatic keyphrase extraction makes
it feasible to generate keyphrases for the huge number of documents that do not
have manually assigned keyphrases. A limitation of previous keyphrase
extraction algorithms is that the selected keyphrases are occasionally
incoherent. That is, the majority of the output keyphrases may fit together
well, but there may be a minority that appear to be outliers, with no clear
semantic relation to the majority or to each other. This paper presents
enhancements to the Kea keyphrase extraction algorithm that are designed to
increase the coherence of the extracted keyphrases. The approach is to use the
degree of statistical association among candidate keyphrases as evidence that
they may be semantically related. The statistical association is measured using
web mining. Experiments demonstrate that the enhancements improve the quality
of the extracted keyphrases. Furthermore, the enhancements are not
domain-specific: the algorithm generalizes well when it is trained on one
domain (computer science documents) and tested on another (physics documents).Comment: 6 pages, related work available at http://purl.org/peter.turney
Factorizing LambdaMART for cold start recommendations
Recommendation systems often rely on point-wise loss metrics such as the mean
squared error. However, in real recommendation settings only few items are
presented to a user. This observation has recently encouraged the use of
rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to
rank which relies on such a metric. Despite its success it does not have a
principled regularization mechanism relying in empirical approaches to control
model complexity leaving it thus prone to overfitting.
Motivated by the fact that very often the users' and items' descriptions as
well as the preference behavior can be well summarized by a small number of
hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization
(LambdaMART-MF), that learns a low rank latent representation of users and
items using gradient boosted trees. The algorithm factorizes lambdaMART by
defining relevance scores as the inner product of the learned representations
of the users and items. The low rank is essentially a model complexity
controller; on top of it we propose additional regularizers to constraint the
learned latent representations that reflect the user and item manifolds as
these are defined by their original feature based descriptors and the
preference behavior. Finally we also propose to use a weighted variant of NDCG
to reduce the penalty for similar items with large rating discrepancy.
We experiment on two very different recommendation datasets, meta-mining and
movies-users, and evaluate the performance of LambdaMART-MF, with and without
regularization, in the cold start setting as well as in the simpler matrix
completion setting. In both cases it outperforms in a significant manner
current state of the art algorithms
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