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