instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Motivated by the previously developed multilevel aggregation method for solving structural analysis problems a novel two-level aggregation approach for efficient iterative solution of Principal Component Analysis (PCA) problems is proposed. The course aggregation model of the original covariance matrix is used in the iterative solution of the eigenvalue problem by a power iterations method. The method is tested on several data sets consisting of large number of text documents.
answer:
ITERATIVE AGGREGATION METHOD FOR SOLVING PRINCIPAL COMPONENT ANALYSIS PROBLEMS


question:
Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRTbased proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student’s future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNNbased method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.
answer:
Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation


question:
Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention over the last few years, resulting in the construction of several KGs, such as NELL, Google Knowledge Vault, etc. These KGs consist of thousands of ‘predicate-relations’ (e.g., isPerson, isMayorOf ) and millions of their instances (e.g., (Bill de Blasio, isMayorOf, New York City)). Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. Even though crowdsourcing is an obvious choice for such evaluation, the standard single-task crowdsourcing, where each predicate in the KG is evaluated independently, is very expensive and especially problematic if the budget available is limited. We show that such approaches are sub-optimal as they ignore dependencies among various predicates and their instances. To overcome this challenge, we propose Relational Crowdsourcing (RelCrowd), where the tasks are created while taking dependencies among predicates and instances into account. We apply this framework in the context of evaluation of large-scale KGs and demonstrate its effectiveness through extensive experiments on real-world datasets.
answer:

Relational Crowdsourcing and its Application in Knowledge Graph Evaluation