@inproceedings{liang-etal-2023-graph,
title = "Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning",
author = "Liang, Ziran and
Lu, Yuyin and
Chen, HeGang and
Rao, Yanghui",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.790",
doi = "10.18653/v1/2023.acl-long.790",
pages = "14133--14149",
abstract = "The out-of-vocabulary (OOV) words are difficult to represent while critical to the performance of embedding-based downstream models. Prior OOV word embedding learning methods failed to model complex word formation well. In this paper, we propose a novel graph-based relation mining method, namely GRM, for OOV word embedding learning. We first build a Word Relationship Graph (WRG) based on word formation and associate OOV words with their semantically relevant words, which can mine the relational information inside word structures. Subsequently, our GRM can infer high-quality embeddings for OOV words through passing and aggregating semantic attributes and relational information in the WRG, regardless of contextual richness. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baselines on both intrinsic and downstream tasks when faced with OOV words.",
}
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<abstract>The out-of-vocabulary (OOV) words are difficult to represent while critical to the performance of embedding-based downstream models. Prior OOV word embedding learning methods failed to model complex word formation well. In this paper, we propose a novel graph-based relation mining method, namely GRM, for OOV word embedding learning. We first build a Word Relationship Graph (WRG) based on word formation and associate OOV words with their semantically relevant words, which can mine the relational information inside word structures. Subsequently, our GRM can infer high-quality embeddings for OOV words through passing and aggregating semantic attributes and relational information in the WRG, regardless of contextual richness. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baselines on both intrinsic and downstream tasks when faced with OOV words.</abstract>
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%0 Conference Proceedings
%T Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning
%A Liang, Ziran
%A Lu, Yuyin
%A Chen, HeGang
%A Rao, Yanghui
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liang-etal-2023-graph
%X The out-of-vocabulary (OOV) words are difficult to represent while critical to the performance of embedding-based downstream models. Prior OOV word embedding learning methods failed to model complex word formation well. In this paper, we propose a novel graph-based relation mining method, namely GRM, for OOV word embedding learning. We first build a Word Relationship Graph (WRG) based on word formation and associate OOV words with their semantically relevant words, which can mine the relational information inside word structures. Subsequently, our GRM can infer high-quality embeddings for OOV words through passing and aggregating semantic attributes and relational information in the WRG, regardless of contextual richness. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baselines on both intrinsic and downstream tasks when faced with OOV words.
%R 10.18653/v1/2023.acl-long.790
%U https://aclanthology.org/2023.acl-long.790
%U https://doi.org/10.18653/v1/2023.acl-long.790
%P 14133-14149
Markdown (Informal)
[Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning](https://aclanthology.org/2023.acl-long.790) (Liang et al., ACL 2023)
ACL