[PDF] Top 20 Semantic indexing with deep learning: a case study
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Semantic indexing with deep learning: a case study
... Table 1 and Fig. 3 shows the results of biomedical abstract semantic indexing. Experi- mental analysis indicates that DBC_flat classification using CNNs (where each class as a binary classification problem) ... See full document
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A case study on sepsis using PubMed and Deep Learning for ontology learning
... Ontology LEarning Ontology for Sepsis (POLEOS) – refers to the OWL-DL ontology built programmatically out of the n top-ranked candidate terms obtained from each model ...UMLS Semantic Types and Groups ... See full document
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Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase
... in semantic similarity datasets, where a pair of words or sentences is labeled on a gradient classification ...cases, semantic similarity tasks overlap with paraphrase detection, as in Xu et ... See full document
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A case study in semantic deconstruction
... of semantic deconstruc- tion, whereby sentences and words are broken down into smaller units so that their true meaning may come to ...although semantic deconstruction is apparently driv- en by the need to ... See full document
9
Blood Cell Count Using Deep Learning Semantic Segmentation
... For the blood cell counting phase, according to literature, few works of blood cell counting system that are count simultaneous the number of WBCs and RBCs. Research in [4] utilized various conventional image processing ... See full document
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Segmentation of left ventricle in 2D echocardiography using deep learning
... This study aims to adapt and evaluate the performance of different state-of-the-art deep learning semantic segmentation methods to segment the LV border on 2D echocardiography images ... See full document
9
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization
... Distributional Semantic Reward During eval- uating the quality of the generated sentences, R OUGE looks for exact matches between refer- ences and generations, which naturally overlooks the expression diversity of ... See full document
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Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
... this study, we utilize the convolutional layers merely as a feature ...in learning discriminative feature representations for the images in our ...well-known deep convolutional neural network ... See full document
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Recognizing UMLS Semantic Types with Deep Learning
... which never appears in the full MedMentions dataset. Approximately 8% of the concepts in UMLS can be linked to more than one seman- tic type (Mohan and Li, 2019); in such cases the dataset contains a comma-separated list ... See full document
11
Towards Semi Supervised Learning for Deep Semantic Role Labeling
... We evaluate our model’s performance on span- based SRL dataset from CoNLL-2012 shared task (Pradhan et al., 2013). This dataset con- tains gold predicates as part of the input sen- tence and also gold parse information ... See full document
6
Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach
... labels indicates that if the predicted labels are augmented with their parents with distance 1, the number of common labels between true labels and predicted ones will increase on average by 0.0256 over all documents in ... See full document
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Semantic and Contextual Proximities for Informal Learning: The Case Study of Museum Visits
... a semantic representation of works which gives them a certain ...a semantic model of the domain, that is to say, the cul- tural ...of semantic proximities permits to provide opportunities for ... See full document
8
Applying deep matching networks to Chinese medical question answering: a study and a dataset
... As for QA in open-domain, researchers have displayed meaningful work to select answers by semantic match- ing in various level. Hu et al. propose ARC-I and ARC-II, which first conducted word-level matching between ... See full document
10
Advanced Machine Learning Approach: Deep Learning
... machine learning is undergoing its golden age as deep learning becomes gradually the pioneer in this ...field. Deep learning uses multiple layers to represent information abstractions ... See full document
5
Semantic search using Latent Semantic Indexing and Word Net
... Latent Semantic Indexing is a technique that can be employed to overcome this ...an indexing and retrieval method that makes use of a mathematical technique called Singular Value Decomposition to ... See full document
5
Adult Learners’ Participation In A Blended Learning Environment: A Case Study On Imposed Pace Learning
... designing learning environments for adults to create solutions for more flexibility that also promotes ...In learning environments with high attendance rates, satisfaction, and motivation, lower dropout and ... See full document
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Development and Evaluation of an Educational Intervention to Enhance Deep Learning and Study Skills among Pharmacy Students in Zambia
... strategic learning remained seemingly more preferred among ...a deep ATL and study skills which the student may opt to or not adopt in their learning ...a deep ATL has been shown to ... See full document
8
Bilingual Document Alignment with Latent Semantic Indexing
... 4.1 Recall on training and test data To rank alignment hypotheses, we investigated all uniform linear combinations of the three individ- ual scoring functions. Table 1 shows the results for the training set, and, in the ... See full document
5
THE EFFECTS OF TECHNOLOGY, ORGANISATIONAL, BEHAVIOURAL FACTORS TOWARDS UTILIZATION OF E GOVERNMENT ADOPTION MODEL BY MODERATING CULTURAL FACTORS
... Semantic Analysis is explored outside of natural language applications and applied to data from several fields like security and finance, where the idea of ‘concepts’ defined through first and second-order occurrence ... See full document
11
Adversarial Learning Based Semantic Correlation Representation for Cross-Modal Retrieval
... technique, deep learning [39–41] is used to overcome several challenges of multimedia analysis and retrieval, such as image classification [42], object recognition [43], video retrievsal [44], ... See full document
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