2,272,245 research outputs found
Deep Short Text Classification with Knowledge Powered Attention
Short text classification is one of important tasks in Natural Language
Processing (NLP). Unlike paragraphs or documents, short texts are more
ambiguous since they have not enough contextual information, which poses a
great challenge for classification. In this paper, we retrieve knowledge from
external knowledge source to enhance the semantic representation of short
texts. We take conceptual information as a kind of knowledge and incorporate it
into deep neural networks. For the purpose of measuring the importance of
knowledge, we introduce attention mechanisms and propose deep Short Text
Classification with Knowledge powered Attention (STCKA). We utilize Concept
towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS)
attention to acquire the weight of concepts from two aspects. And we classify a
short text with the help of conceptual information. Unlike traditional
approaches, our model acts like a human being who has intrinsic ability to make
decisions based on observation (i.e., training data for machines) and pays more
attention to important knowledge. We also conduct extensive experiments on four
public datasets for different tasks. The experimental results and case studies
show that our model outperforms the state-of-the-art methods, justifying the
effectiveness of knowledge powered attention
Measuring concept similarities in multimedia ontologies: analysis and evaluations
The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing
Libraries and Information Systems Need XML/RDF... but Do They Know It?
This article presents an approach to the uses of XML (eXtensible Markup Language) and Semantic Web technologies in
the field of information services, focusing mainly on the creation and management of digital libraries compared to traditional
libraries, while paying special attention to the concept and application of metadata, and RDF based integration
The GIST of Concepts
A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three\ud
experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Objective: To compare different deep learning architectures for predicting
the risk of readmission within 30 days of discharge from the intensive care
unit (ICU). The interpretability of attention-based models is leveraged to
describe patients-at-risk. Methods: Several deep learning architectures making
use of attention mechanisms, recurrent layers, neural ordinary differential
equations (ODEs), and medical concept embeddings with time-aware attention were
trained using publicly available electronic medical record data (MIMIC-III)
associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was
used to compute the posterior over weights of an attention-based model. Odds
ratios associated with an increased risk of readmission were computed for
static variables. Diagnoses, procedures, medications, and vital signs were
ranked according to the associated risk of readmission. Results: A recurrent
neural network, with time dynamics of code embeddings computed by neural ODEs,
achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score:
0.372). Predictive accuracy was comparable across neural network architectures.
Groups of patients at risk included those suffering from infectious
complications, with chronic or progressive conditions, and for whom standard
medical care was not suitable. Conclusions: Attention-based networks may be
preferable to recurrent networks if an interpretable model is required, at only
marginal cost in predictive accuracy
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