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

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

URL: http://arxiv.org/abs/2208.05462v1
Date: Wed, 10 Aug 2022 17:40:45 GMT
Title: The Analysis about Building Cross-lingual Sememe Knowledge Base Based on Deep Clustering Network
Authors: Xiaoran Li and Toshiaki Takano
Abstract summary: Sememe knowledge bases (KBs) contain words annotated with sememes. We propose an unsupervised method based on a deep clustering network (DCN) to build a sememe KB.
Score: 0.7310043452300736
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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