Bilingual Abstract Semantic Associative Network Training (BAbSANT): A Polish-English Case Study

2021 ◽  
pp. 106143
Author(s):  
Chaleece W. Sandberg ◽  
Monika Zacharewicz ◽  
Teresa Gray
Keyword(s):  
Associative Network ◽  
Network Training

Abstract Semantic Associative Network Training: A Replication and Update of an Abstract Word Retrieval Therapy Program

2020 ◽  
Vol 29 (3) ◽  
pp. 1574-1595
Author(s):  
Chaleece W. Sandberg ◽  
Teresa Gray
Keyword(s):  
Concrete Word ◽  
Abstract Word ◽  
Distinct Pattern ◽  
Word Retrieval ◽  
Effect Sizes ◽  
Associative Network ◽  
Direct Training ◽  
Network Training ◽  
Abstract Words ◽  
Concrete Words

Purpose We report on a study that replicates previous treatment studies using Abstract Semantic Associative Network Training (AbSANT), which was developed to help persons with aphasia improve their ability to retrieve abstract words, as well as thematically related concrete words. We hypothesized that previous results would be replicated; that is, when abstract words are trained using this protocol, improvement would be observed for both abstract and concrete words in the same context-category, but when concrete words are trained, no improvement for abstract words would be observed. We then frame the results of this study with the results of previous studies that used AbSANT to provide better evidence for the utility of this therapeutic technique. We also discuss proposed mechanisms of AbSANT. Method Four persons with aphasia completed one phase of concrete word training and one phase of abstract word training using the AbSANT protocol. Effect sizes were calculated for each word type for each phase. Effect sizes for this study are compared with the effect sizes from previous studies. Results As predicted, training abstract words resulted in both direct training and generalization effects, whereas training concrete words resulted in only direct training effects. The reported results are consistent across studies. Furthermore, when the data are compared across studies, there is a distinct pattern of the added benefit of training abstract words using AbSANT. Conclusion Treatment for word retrieval in aphasia is most often aimed at concrete words, despite the usefulness and pervasiveness of abstract words in everyday conversation. We show the utility of AbSANT as a means of improving not only abstract word retrieval but also concrete word retrieval and hope this evidence will help foster its application in clinical practice.

Tutorial for Abstract Semantic Associative Network Training (AbSANT): Theoretical Rationale, Step-by-Step Protocol, and Material Resources

2022 ◽  
pp. 1-10
Author(s):  
Chaleece W. Sandberg
Keyword(s):  
Decision Making ◽  
Evidence Based ◽  
Theoretical Rationale ◽  
Associative Network ◽  
Material Resources ◽  
Link Type ◽  
Network Training ◽  
Informed Decisions ◽  
Abstract Words ◽  
Concrete Words

Purpose: The availability of evidence-based therapies for abstract words is limited. Abstract Semantic Associative Network Training (AbSANT) is theoretically motivated and has been shown to not only improve directly trained abstract words, such as the word emergency in the category hospital, but also promote generalization to related concrete words, such as the word doctor . Method: This tutorial provides step-by-step instructions, including cueing strategies, and material resources for conducting AbSANT. Importantly, this tutorial also explains the theoretical motivation behind AbSANT, as well as information regarding the population, dose, and environment characteristics of effective trials, to help clinicians make informed decisions regarding the applicability of this approach and to guide decision-making throughout the steps of therapy. Conclusions: AbSANT is an effective, theoretically based treatment for abstract words. This tutorial provides all of the resources needed to conduct AbSANT with clients with aphasia. Supplemental Material https://doi.org/10.23641/asha.17776211

A comparative case study of neural network training by using frame-level cost functions for automatic speech recognition purposes in Spanish

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19669-19715
Author(s):  
Aldonso Becerra ◽  
J. Ismael de la Rosa ◽  
Efrén González ◽  
A. David Pedroza ◽  
N. Iracemi Escalante ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Cost Functions ◽  
Comparative Case Study ◽  
Neural Network Training ◽  
Network Training

Analogy as driving force of language change: a usage-based approach to wo and da clauses in 17th and 18th century German

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Melitta Gillmann
Keyword(s):  
Driving Force ◽  
Language Change ◽  
18Th Century ◽  
Mechanisms Of Change ◽  
Mutual Attraction ◽  
Associative Network ◽  
High Level

Abstract This paper presents a case study conducted on 17th and 18th century German corpora, confirming that both attraction and differentiation are important mechanisms of change, which interact with socio-symbolic properties of constructions. The paper looks at the frequencies and semantics of wo ‘where’ clauses at the beginning of the New High German period, which are compared to the frequencies and semantics of the connector da ‘there, since’ in the same period. The study reveals that the subordinating connectors wo and da overlapped in their functions and were highly polysemous (or semantically vague), establishing spatial, temporal, causal, conditional, and contrast links between clauses. This suggests that the connectors had become functionally similar by means of mutual attraction; however, they differed in that they belonged to different registers. Over the course of the 18th century, the polysemy of wo and da clauses reduced. Being gradually confined to one single meaning, the connectors became less similar. This differentiation occurs because the connectors aligned to distinct high-level schemas in the associative network. The study confirms that analogy is crucial to both attraction and differentiation of functionally overlapping constructions. While attraction involves analogy of specific instances of constructions, differentiation occurs in analogy to high-level abstract constructions in the associative network.

scholarly journals Enhanced Day-Ahead PV Power Forecast: Dataset Clustering for an Effective Artificial Neural Network Training

2021 ◽  
Vol 5 (1) ◽  
pp. 16
Author(s):  
Andrea Matteri ◽  
Emanuele Ogliari ◽  
Alfredo Nespoli
Keyword(s):  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Energy Sources ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Network Training ◽  
Artificial Neural ◽  
Grid Operation ◽  
Artificial Neural Network Training

The increasing integration of renewable energy sources into the existing energy supply structure is challenging due to the intermittency typical of these energy sources, which implies problems of reliability and scheduling of grid operation. Concerning solar energy, the solar forecast tool predicts the photovoltaic (PV) power production and therefore permits a more efficient grid management. In this paper, the combination of clustering techniques and ANNs (Artificial Neural Networks) for day-ahead PV power forecast is analyzed. Clustering techniques are exploited to divide a dataset into different classes of days with similar weather conditions. Then, a dedicated ANN is developed for every group. The main goal is to assess the forecast improvement determined by the combination of ANNs and dataset clustering methods. Different combinations are compared on a real case study: a PV facility in SolarTechLAB, in Politecnico di Milano.

scholarly journals Self-Supervised Voltage Sag Source Identification Method Based on CNN

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1059 ◽  
Author(s):  
Danqi Li ◽  
Fei Mei ◽  
Chenyu Zhang ◽  
Haoyuan Sha ◽  
Jianyong Zheng
Keyword(s):  
Neural Network ◽  
Source Identification ◽  
The Self ◽  
Convolution Neural Network ◽  
Voltage Sag ◽  
Training Process ◽  
Identification Method ◽  
Network Training ◽  
Three Phase

A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison.

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