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Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion ...
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Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings ...
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Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings
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I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews ...
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Detect and Classify – Joint Span Detection and Classification for Health Outcomes ...
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Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance ...
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Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications
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In: Comput Math Methods Med (2021)
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Abstract:
Word embedding models have recently shown some capability to encode hierarchical information that exists in textual data. However, such models do not explicitly encode the hierarchical structure that exists among words. In this work, we propose a method to learn hierarchical word embeddings (HWEs) in a specific order to encode the hierarchical information of a knowledge base (KB) in a vector space. To learn the word embeddings, our proposed method considers not only the hypernym relations that exist between words in a KB but also contextual information in a text corpus. The experimental results on various applications, such as supervised and unsupervised hypernymy detection, graded lexical entailment prediction, hierarchical path prediction, and word reconstruction tasks, show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning nonspecialised, hypernym-specific, and hierarchical word embeddings on multiple benchmarks.
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Keyword:
Research Article
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8610673/ https://doi.org/10.1155/2021/9761163
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RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding.
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Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
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Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
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Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction ...
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Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ...
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Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction.
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Learning to Compose Relational Embeddings in Knowledge Graphs
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