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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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Abstract:
Given multiple source word embeddings learnt using diverse algorithms and lexical resources, meta word embedding learning methods attempt to learn more accurate and wide-coverage word embeddings. Prior work on meta-embedding has repeatedly discovered that simple vector concatenation of the source embeddings to be a competitive baseline. However, it remains unclear as to why and when simple vector concatenation can produce accurate meta-embeddings. We show that weighted concatenation can be seen as a spectrum matching operation between each source embedding and the meta-embedding, minimising the pairwise inner-product loss. Following this theoretical analysis, we propose two \emph{unsupervised} methods to learn the optimal concatenation weights for creating meta-embeddings from a given set of source embeddings. Experimental results on multiple benchmark datasets show that the proposed weighted concatenated meta-embedding methods outperform previously proposed meta-embedding learning methods. ... : Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022) ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2204.12386 https://dx.doi.org/10.48550/arxiv.2204.12386
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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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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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