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A sememe is defined as the minimum semantic unit of human languages. Sememeknowledge bases (KBs), which contain words annotated with sememes, have beensuccessfully applied to many NLP tasks, and we believe that by learning thesmallest unit of meaning, computers can more easily understand human language.However, Existing sememe KBs are built on only manual annotation, humanannotations have personal understanding biases, and the meaning of vocabularywill be constantly updated and changed with the times, and artificial methodsare not always practical. To address the issue, we propose an unsupervisedmethod based on a deep clustering network (DCN) to build a sememe KB, and youcan use any language to build a KB through this method. We first learn thedistributed representation of multilingual words, use MUSE to align them in asingle vector space, learn the multi-layer meaning of each word through theself-attention mechanism, and use a DNC to cluster sememe features. Finally, wecompleted the prediction using only the 10-dimensional sememe space in English.We found that the low-dimensional space can still retain the main feature ofthe sememes.

The Analysis about Building Cross-lingual Sememe Knowledge Base Based on Deep Clustering Network