Papers by Ziran Liang

1 papers
Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning (2023.acl-long)

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Challenge: Existing word embedding methods fail to model complex word formation well.
Approach: They propose a graph-based relation mining method for OOV word embedding learning that can infer high-quality embeddables for OV words through passing and aggregating semantic attributes and relational information in the WRG.
Outcome: The proposed method outperforms state-of-the-art models on both intrinsic and downstream tasks when faced with OOV words.

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