  In linguistics, a sememe is defined as the minimum semantic unit of
languages. Sememe knowledge bases (KBs), which are built by manually annotating
words with sememes, have been successfully applied to various NLP tasks.
However, existing sememe KBs only cover a few languages, which hinders the wide
utilization of sememes. To address this issue, the task of sememe prediction
for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe
KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically
predicting sememes for a BabelNet synset, the words in many languages in the
synset would obtain sememe annotations simultaneously. However, previous SPBS
methods have not taken full advantage of the abundant information in BabelNet.
In this paper, we utilize the multilingual synonyms, multilingual glosses and
images in BabelNet for SPBS. We design a multimodal information fusion model to
encode and combine this information for sememe prediction. Experimental results
show the substantial outperformance of our model over previous methods (about
10 MAP and F1 scores). All the code and data of this paper can be obtained at
https://github.com/thunlp/MSGI.
