반응형 논문572 [2023-02-07] 오늘의 자연어처리 Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs Knowledge graphs (KGs) have become effective knowledge resources in diverse applications, and knowledge graph embedding (KGE) methods have attracted increasing attention in recent years. However, it's still challenging for conventional KGE methods to handle unseen entities or relations during the model test.. 2023. 2. 7. [2023-02-06] 오늘의 자연어처리 Unsupervised Entity Alignment for Temporal Knowledge Graphs Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that th.. 2023. 2. 6. [2023-02-05] 오늘의 자연어처리 Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets Although recipe data are very easy to come by nowadays, it is really hard to find a complete recipe dataset - with a list of ingredients, nutrient values per ingredient, and per recipe, allergens, etc. Recipe datasets are usually collected from social media websites where users post .. 2023. 2. 5. [2023-02-04] 오늘의 자연어처리 How to choose "Good" Samples for Text Data Augmentation Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation to expand the corpus size. However, data augmentation may potentially produce some noisy au.. 2023. 2. 4. 이전 1 ··· 86 87 88 89 90 91 92 ··· 143 다음 반응형