The Combined Method of Semantic Similarity Estimation of Problem Oriented Knowledge on the Basis of Evolutionary Procedures

2017 ◽  
pp. 74-83 ◽  
Author(s):  
V. V. Bova ◽  
E. V. Nuzhnov ◽  
V. V. Kureichik
Keyword(s):  
Semantic Similarity ◽  
Combined Method ◽  
Similarity Estimation

scholarly journals Semantic Similarity Estimation Using Vector Symbolic Architectures

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109120-109132
Author(s):  
Job Isaias Quiroz-Mercado ◽  
Ricardo Barron-Fernandez ◽  
Marco Antonio Ramirez-Salinas
Keyword(s):  
Semantic Similarity ◽  
Similarity Estimation

scholarly journals Neural sentence embedding models for semantic similarity estimation in the biomedical domain

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kathrin Blagec ◽  
Hong Xu ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Semantic Similarity ◽  
Biomedical Domain ◽  
Similarity Estimation

Graph-based Multimodal Ranking Models for Multimodal Summarization

2021 ◽  
Vol 20 (4) ◽  
pp. 1-21
Author(s):  
Junnan Zhu ◽  
Lu Xiang ◽  
Yu Zhou ◽  
Jiajun Zhang ◽  
Chengqing Zong
Keyword(s):  
Multimedia Information ◽  
Semantic Similarity ◽  
Rapid Growth ◽  
Multimedia Data ◽  
Evaluation Metrics ◽  
Unified Framework ◽  
Estimation Model ◽  
Similarity Estimation ◽  
Ranking Models ◽  
Human Evaluation

Multimodal summarization aims to extract the most important information from the multimedia input. It is becoming increasingly popular due to the rapid growth of multimedia data in recent years. There are various researches focusing on different multimodal summarization tasks. However, the existing methods can only generate single-modal output or multimodal output. In addition, most of them need a lot of annotated samples for training, which makes it difficult to be generalized to other tasks or domains. Motivated by this, we propose a unified framework for multimodal summarization that can cover both single-modal output summarization and multimodal output summarization. In our framework, we consider three different scenarios and propose the respective unsupervised graph-based multimodal summarization models without the requirement of any manually annotated document-summary pairs for training: (1) generic multimodal ranking, (2) modal-dominated multimodal ranking, and (3) non-redundant text-image multimodal ranking. Furthermore, an image-text similarity estimation model is introduced to measure the semantic similarity between image and text. Experiments show that our proposed models outperform the single-modal summarization methods on both automatic and human evaluation metrics. Besides, our models can also improve the single-modal summarization with the guidance of the multimedia information. This study can be applied as the benchmark for further study on multimodal summarization task.

Semantic similarity estimation from multiple ontologies

2012 ◽  
Vol 38 (1) ◽  
pp. 29-44 ◽  
Author(s):  
Montserrat Batet ◽  
David Sánchez ◽  
Aida Valls ◽  
Karina Gibert
Keyword(s):  
Semantic Similarity ◽  
Similarity Estimation ◽  
Multiple Ontologies

scholarly journals Enabling semantic similarity estimation across multiple ontologies: An evaluation in the biomedical domain

2012 ◽  
Vol 45 (1) ◽  
pp. 141-155 ◽  
Author(s):  
David Sánchez ◽  
Albert Solé-Ribalta ◽  
Montserrat Batet ◽  
Francesc Serratosa
Keyword(s):  
Semantic Similarity ◽  
Biomedical Domain ◽  
Similarity Estimation ◽  
Multiple Ontologies
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