1,210 research outputs found
Probabilistic models of information retrieval based on measuring the divergence from randomness
We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose--Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document--query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model
Quantifying dependencies for sensitivity analysis with multivariate input sample data
We present a novel method for quantifying dependencies in multivariate
datasets, based on estimating the R\'{e}nyi entropy by minimum spanning trees
(MSTs). The length of the MSTs can be used to order pairs of variables from
strongly to weakly dependent, making it a useful tool for sensitivity analysis
with dependent input variables. It is well-suited for cases where the input
distribution is unknown and only a sample of the inputs is available. We
introduce an estimator to quantify dependency based on the MST length, and
investigate its properties with several numerical examples. To reduce the
computational cost of constructing the exact MST for large datasets, we explore
methods to compute approximations to the exact MST, and find the multilevel
approach introduced recently by Zhong et al. (2015) to be the most accurate. We
apply our proposed method to an artificial testcase based on the Ishigami
function, as well as to a real-world testcase involving sediment transport in
the North Sea. The results are consistent with prior knowledge and heuristic
understanding, as well as with variance-based analysis using Sobol indices in
the case where these indices can be computed
Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval
Although more and more language pairs are covered by machine translation
services, there are still many pairs that lack translation resources.
Cross-language information retrieval (CLIR) is an application which needs
translation functionality of a relatively low level of sophistication since
current models for information retrieval (IR) are still based on a
bag-of-words. The Web provides a vast resource for the automatic construction
of parallel corpora which can be used to train statistical translation models
automatically. The resulting translation models can be embedded in several ways
in a retrieval model. In this paper, we will investigate the problem of
automatically mining parallel texts from the Web and different ways of
integrating the translation models within the retrieval process. Our
experiments on standard test collections for CLIR show that the Web-based
translation models can surpass commercial MT systems in CLIR tasks. These
results open the perspective of constructing a fully automatic query
translation device for CLIR at a very low cost.Comment: 37 page
Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval
In this paper, we present a submission to the Touche lab's Task 2 on Argument
Retrieval for Comparative Questions. Our team Katana supplies several
approaches based on decision tree ensembles algorithms to rank comparative
documents in accordance with their relevance and argumentative support. We use
PyTerrier library to apply ensembles models to a ranking problem, considering
statistical text features and features based on comparative structures. We also
employ large contextualized language modelling techniques, such as BERT, to
solve the proposed ranking task. To merge this technique with ranking
modelling, we leverage neural ranking library OpenNIR.
Our systems substantially outperforming the proposed baseline and scored
first in relevance and second in quality according to the official metrics of
the competition (for measure NDCG@5 score). Presented models could help to
improve the performance of processing comparative queries in information
retrieval and dialogue systems
The Discrete Infinite Logistic Normal Distribution
We present the discrete infinite logistic normal distribution (DILN), a
Bayesian nonparametric prior for mixed membership models. DILN is a
generalization of the hierarchical Dirichlet process (HDP) that models
correlation structure between the weights of the atoms at the group level. We
derive a representation of DILN as a normalized collection of gamma-distributed
random variables, and study its statistical properties. We consider
applications to topic modeling and derive a variational inference algorithm for
approximate posterior inference. We study the empirical performance of the DILN
topic model on four corpora, comparing performance with the HDP and the
correlated topic model (CTM). To deal with large-scale data sets, we also
develop an online inference algorithm for DILN and compare with online HDP and
online LDA on the Nature magazine, which contains approximately 350,000
articles.Comment: This paper will appear in Bayesian Analysis. A shorter version of
this paper appeared at AISTATS 2011, Fort Lauderdale, FL, US
Quantifying dependencies for sensitivity analysis with multivariate input sample data
We present a novel method for quantifying dependencies in multivariate datasets, based on estimating the Rényi entropy by minimum spanning trees (MSTs). The length of the MSTs can be used to order pairs of variables from strongly to weakly dependent, making it a useful tool for sensitivity analysis with dependent input variables. It is well-suited for cases where the input distribution is unknown and only a sample of the inputs is available. We introduce an estimator to quantify dependency based on the MST length, and investigate its properties with several numerical examples. To reduce the computational cost of constructing the exact MST for large datasets, we explore methods to compute approximations to the exact MST, and find the multilevel approach introduced recently by Zhong et al. (2015) to be the most accurate. We apply our proposed method to an artificial testcase based on the Ishigami function, as well as to a real-world testcase involving sediment transport in the North Sea. The results are consistent with prior knowledge and heuristic understanding, as well as with variance-based analysis using Sobol indices in the case where these indices can be computed
- …