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1
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion ...
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2
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings ...
Bollegala, Danushka. - : arXiv, 2022
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3
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings
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4
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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5
Detect and Classify – Joint Span Detection and Classification for Health Outcomes ...
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6
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance ...
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7
Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications
In: Comput Math Methods Med (2021)
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8
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding.
Bollegala, Danushka; Kawarabayashi, Ken-ichi; Yoshida, Yuichi. - : Association for Computational Linguistics, 2021
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9
Dictionary-based Debiasing of Pre-trained Word Embeddings.
Bollegala, Danushka; Kaneko, Masahiro. - : Association for Computational Linguistics, 2021
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10
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
Sakata, Ichiro; Mori, Junichiro; Bollegala, Danushka. - : Massachusetts Institute of Technology Press, 2021
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11
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
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12
Debiasing Pre-trained Contextualised Embeddings.
Kaneko, Masahiro; Bollegala, Danushka. - : Association for Computational Linguistics, 2021
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13
Autoencoding Improves Pre-trained Word Embeddings ...
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14
Autoencoding Improves Pre-trained Word Embeddings ...
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15
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction ...
Abstract: We propose in this paper a contextualised graph convolution network over multiple dependency sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency graph is proposed. Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction. Our experimental results show that the proposed method achieves superior performance over existing GCN-based models achieving state-of-the-art performance on cross-sentence n-ary relation extraction and SemEval 2010 Task 8 sentence-level relation extraction task. Our model also achieves a comparable performance to the SoTA on the TACRED dataset. ...
Keyword: Computer and Information Science; Information and Knowledge Engineering; Intelligent System; Natural Language Processing; Neural Network
URL: https://underline.io/lecture/9015-graph-convolution-over-multiple-dependency-sub-graphs-for-relation-extraction
https://dx.doi.org/10.48448/ef5g-9703
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16
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ...
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17
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction.
Mandya, Angrosh; Coenen, Frans; Bollegala, Danushka. - : International Committee on Computational Linguistics, 2020
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18
Multi-Source Attention for Unsupervised Domain Adaptation.
Bollegala, Danushka; Cui, Xia. - : Association for Computational Linguistics, 2020
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19
Learning to Compose Relational Embeddings in Knowledge Graphs
Hakami, Huda; Chen, Wenye; Bollegala, Danushka. - : Springer Singapore, 2020
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20
Tree-Structured Neural Topic Model
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