Dataset Information

Towards a Multi-level Exploration of Human and Computational Re-representation in Unified Cognitive Frameworks.

ABSTRACT: Re-representation is a critical ability to (i) understanding human creative problem solving, and (ii) modeling computational cognitive systems able to support or perform creative problem solving tasks on their own. This paper proposes a unified multi-level cognitive approach to investigating re-representation: the study of sensory-based, concept-based and problem template based possible forms of re-representations in an integrated manner. Descriptions and explanations of each level prepare the ground for further computational modeling. A study is deployed in order to explore the relationship between the various tasks proposed to reflect re-representation. A significant correlation between the investigated tasks is discovered. Two previous studies from the literature are replicated. A new strong and significant relationship between the Pattern Meanings Test and the Alternative Uses Test is observed.

SUBMITTER: Olteteanu AM

PROVIDER: S-EPMC6502907 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Towards a Multi-level Exploration of Human and Computational Re-representation in Unified Cognitive Frameworks.

Olteţeanu Ana-Maria AM Schöttner Mikkel M Bahety Arpit A

Frontiers in psychology 20190430

Re-representation is a critical ability to (i) understanding human creative problem solving, and (ii) modeling computational cognitive systems able to support or perform creative problem solving tasks on their own. This paper proposes a unified multi-level cognitive approach to investigating re-representation: the study of sensory-based, concept-based and problem template based possible forms of re-representations in an integrated manner. Descriptions and explanations of each level prepare the g ...[more]

PMID: 31114526

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Project description:BACKGROUND:Medical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition. OBJECTIVE:This study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities. METHODS:In this paper, the pretraining language representation model was investigated; utilizing information not only from the final layer but from intermediate layers was found to affect the performance of the Chinese medical entity recognition task. Therefore, we proposed a multi-level representation learning model for entity recognition in Chinese EMRs. Specifically, we first used the BERT language model to extract semantic representations. Then, the multi-head attention mechanism was leveraged to automatically extract deeper semantic information from each layer. Finally, semantic representations from multi-level representation extraction were utilized as the final semantic context embedding for each token and we used softmax to predict the entity tags. RESULTS:The best F1 score reached by the experiment was 82.11% when using the CEMR dataset, and the F1 score when using the CCKS (China Conference on Knowledge Graph and Semantic Computing) 2018 benchmark dataset further increased to 83.18%. Various comparative experiments showed that our proposed method outperforms methods from previous work and performs as a new state-of-the-art method. CONCLUSIONS:The multi-level representation learning model is proposed as a method to perform the Chinese EMRs entity recognition task. Experiments on two clinical datasets demonstrate the usefulness of using the multi-head attention mechanism to extract multi-level representation as part of the language model.

Dataset Information

Towards a Multi-level Exploration of Human and Computational Re-representation in Unified Cognitive Frameworks.

Publications

Towards a Multi-level Exploration of Human and Computational Re-representation in Unified Cognitive Frameworks.

Similar Datasets

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