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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Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning

Machine Learning for NLP 2

technical paper

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Ziran Liang

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Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning

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