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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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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.686/ Abstract: Jointly detecting and classifying outcomes (measurements or observations used to capture and assess the effect of a treatment in clinical trials) leverages the relationship between an outcome span mentioned in a clinical trial abstract and it's category or type. Treating the detection process as a token-level task and the classification process as a sentence-level task enables us utilize the structural correspondences between the two levels inorder to successfully simulataneously achieve detection and classification of this biomedical entity. Ultimately, we erase the need of multiple models for each task because we don't compromise the standalone performance and matter of fact outperform standalone performance. We also propose a scalable and flexible approach to align comparable datasets, which we adopt to align arbitrary classifications in prior dataset to standardardised classification for health outcomes. ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/wp21-2p43 https://underline.io/lecture/37662-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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