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Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014

Ignacio Cano, Sameer Singh, Carlos Guestrin
2014 Text Retrieval Conference  
The word embeddings provide accurate and compact summaries of observed entity contexts, further described by topic clusters that are estimated in a non-parametric manner.  ...  Additionally, we associate a staleness measure with each entity and topic cluster, dynamically estimating their temporal relevance.  ...  Acknowledgments This work was supported in part by the Argentine Ministry of Science, Technology and Productive Innovation with the program BEC.AR, and in part by TerraSwarm, one of six centers of STARnet, a  ... 
dblp:conf/trec/CanoSG14 fatcat:nqob74sv55dyzgknx3izdtxcci

Improving relevance feedback in language modeling with score regularization

Fernando D. Diaz
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
We demonstrate that regularization can improve feedback in a language modeling framework.  ...  Whereas true relevance modeling is a non-parametric density estimation method, regularization is a non-parametric function approximation method.  ...  For example, if the only known keywords for a topic retrieve a cohesive, non-relevant cluster, we would like to provide information to remove the entire non-relevant cluster [7] .  ... 
doi:10.1145/1390334.1390515 dblp:conf/sigir/Diaz08a fatcat:dz3e6fubevgbdmgtrqqsoasf5e

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Danila Azzolina, Paola Berchialla, Dario Gregori, Ileana Baldi
2021 International Journal of Environmental Research and Public Health  
Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved.  ...  Given the promising flexibility of non-parametric approaches to the experts' elicitation, more efforts are needed to ensure their diffusion also in applied settings.  ...  In this general framework, another issue is the identification of the main research topics and the definition of the peculiarities of papers using parametric and non-parametric approaches in a clinical  ... 
doi:10.3390/ijerph18041833 pmid:33668623 pmcid:PMC7917693 fatcat:of33kvkusfhb3gp6ajwuuphcje

Multivariate Classification of Drugs using Parametric and Nonparametric Machine Learning Models

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Multiclass classification can be determined efficiently using non-parametric machine learning model.  ...  This paper explores parametric and non-parametric machine learning models to classify administration properties of drugs and its toxicity.  ...  Parametric and Non-parametric Machine Learning Model were applied to the dataset. A.  ... 
doi:10.35940/ijitee.c8740.019320 fatcat:5y3siiulh5cqrbbr2mqbdg5x7u

Item Response Models in Psychometrika and Psychometric Textbooks

Seock-Ho Kim, Minho Kwak, Meina Bian, Zachary Feldberg, Travis Henry, Juyeon Lee, İbrahim Burak Ölmez, Yawei Shen, Yanyan Tan, Victoria Tanaka, Jue Wang, Jiajun Xu (+1 others)
2020 Frontiers in Education  
A new classification based on data types is proposed and discussed.  ...  The usual unidimensional parametric item response theory models for polytomous items were employed in 21 per cent of the articles.  ...  In fact, the class of non-parametric models can be applicable to many other item types. There are many non-parametric IRT models for different types of data as well as models for non-monotone items.  ... 
doi:10.3389/feduc.2020.00063 fatcat:ehcpv6qxlrh47k4x7jy4zdiodu

Statistical precision of information retrieval evaluation

Gordon V. Cormack, Thomas R. Lynam
2006 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06  
We advance a framework for statistical evaluation that uses the same general framework to model other sources of chance variation as a source of input for meta-analysis techniques.  ...  Our model accurately predicts the degree of concordance between system results on randomly selected halves of the TREC-6 ad hoc corpus.  ...  These anomalies may be addressed by using a non-parametric binomial model to predict silver bullets and lead balloons -relevant documents whose properties are not represented at all in the corpus.  ... 
doi:10.1145/1148170.1148262 dblp:conf/sigir/CormackL06 fatcat:uhcf2zqazbfqhinawfbajfufzm

Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes

Zhaochun Ren, Maarten de Rijke
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
Specifically, we present a hierarchical non-parametric model to describe hierarchical relations among topics; this model is used to infer threads of topics as themes from the nested Chinese restaurant  ...  Given a topic of interest, a contrastive theme is a group of opposing pairs of viewpoints.  ...  Non-parametric topic modeling Non-parametric topic models are aimed at handling infinitely many topics; they have received much attention.  ... 
doi:10.1145/2766462.2767713 dblp:conf/sigir/RenR15 fatcat:cnk752hiw5brnmea7ro47dclwy

Short text classification using very few words

Aixin Sun
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
We propose a simple, scalable, and non-parametric approach for short text classification.  ...  It first selects the most representative and topical-indicative words from a given short text as query words, and then searches for a small set of labeled short texts best matching the query words.  ...  SUMMARY AND FUTURE WORK We propose a simple, scalable, and non-parametric approach for short text classification.  ... 
doi:10.1145/2348283.2348511 dblp:conf/sigir/Sun12 fatcat:oeq7nc6gcrgqhju4uphp3mr4lm

Finding structure in noisy text: topic classification and unsupervised clustering

Prem Natarajan, Rohit Prasad, Krishna Subramanian, Shirin Saleem, Fred Choi, Rich Schwartz
2007 International Journal on Document Analysis and Recognition  
In another experiment, we excluded the off-topic messages from training of the GL state in our model. Next, we estimated the non-parametric thresholds from the development set as before.  ...  Figure 1 : 1 Generative model used in our HMM based topic classifier. Figure 2 : 2 param-topic-dep non-param-topic-dep non-param-topic-dep-nochaff %False Acceptances vs.  ... 
doi:10.1007/s10032-007-0057-x fatcat:a44ek5kmjzb23onrbgklmvwnxm

Genomic Clinical Trials and Predictive Medicine

Matthew Schipper
2016 International Statistical Review  
For each of the inference topics, authors review relevant non-parametric Bayesian models and approaches including Dirchlet process (DP) models and variations, Pólya trees, wavelet-based models, neural  ...  This chapter includes non-parametric priors on residual distribution, non-parametric mean functions and fully non-parametric regression (also known as density regression).  ... 
doi:10.1111/insr.12166 fatcat:7k6mrqr5dnhg5bdw3liefltxau

Detecting the Eureka Effect in Complex Search [chapter]

Hui Yang, Jiyun Luo, Christopher Wing
2015 Lecture Notes in Computer Science  
We employ non-parametric regression to model the learning curve that exists in learning-intensive search tasks and report our preliminary findings in observing the Eureka effect in patent prior art finding  ...  In the context of patent prior art finding, we introduce a novel notion of Eureka effect in complex search tasks that leverages the sudden change of user's perceived relevance observable in the log data  ...  Portions of this work were conducted under the umbrella of a larger project at the USPTO.  ... 
doi:10.1007/978-3-319-16354-3_80 fatcat:hrj2in6wsrhkbmgmmhdyreqbzm

Refinery: An Open Source Topic Modeling Web Platform

Dae Il Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth
2017 Journal of machine learning research  
Under the hood, we train Bayesian nonparametric topic models that can adapt model complexity to the provided data with scalable learning algorithms.  ...  We introduce Refinery, an open source platform for exploring large text document collections with topic models.  ...  Acknowledgments Refinery was a recipient of the Knight Prototype Fund in 2014.  ... 
dblp:journals/jmlr/KimSHS17 fatcat:mfokctznprcvxcrlhx4shqb5mi

Bayesian Nonparametrics in Topic Modeling: A Brief Tutorial [article]

Alexander Spangher
2015 arXiv   pre-print
I will show a comparison between different non-parametric models and the current state-of-the-art parametric model, Latent Dirichlet Allocation (LDA).  ...  In this work, I will present a brief tutorial on Bayesian nonparametric methods, especially as they are applied to topic modeling.  ...  I've reviewed some recent implementations of nonparametric models and compared them on a single dataset.  ... 
arXiv:1501.03861v1 fatcat:jbgoycyaynbfno3rqoumpvrlo4

Relevance of the technology acceptance model (TAM) in information management research: a review of selected empirical evidence

Mustapha Osman Opoku, Francis Enu-Kwesi
2020 Pressacademia  
Methodology-A desk study approach was used to review some of the studies that have used the model.  ...  Conclusion-In essence, the conflicting views create inconclusiveness about usage of TAM as a theoretical model.  ...  Table 3 : Research based on Non-Parametric Quantitative Procedures 3 No Author(s) Topic Objective(s) Methodology Sample Size and Findings/Results and Year Statistical Technique the Area of Study IT acceptance  ... 
doi:10.17261/pressacademia.2020.1186 fatcat:d53cbgwylzf65igfb5n2nekqb4

Biotechnology and Non-Bayesian Calculations for the Talus Bone Volume

Ahmed Al-Imam
2019 Biomedical Journal of Scientific & Technical Research  
Experiment with a hybrid of non-Bayesian statistical methods, including parametric and non-parametric models of hypothesis testing, in an aim to compute the talus volume and to infer an extrapolation for  ...  Toconclude with a rigour statistical inference on the right versus left tali comparison, we shall implement a plethora of non-Bayesian models of statistics for between-subjects testing, including Fisher's  ... 
doi:10.26717/bjstr.2019.21.003620 fatcat:izmpvrxdcbaflmqfs5uhfxjplu
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