Most of the existing methods for bilingual word embedding only consider
shallow context or simple co-occurrence information. In this paper, we propose
a latent bilingual sense unit (Bilingual Sense Clique, BSC), which is derived
from a maximum complete sub-graph of pointwise mutual information based graph
over bilingual corpus. In this way, we treat source and target words equally
and a separated bilingual projection processing that have to be used in most
existing works is not necessary any more. Several dimension reduction methods
are evaluated to summarize the BSC-word relationship. The proposed method is
evaluated on bilingual lexicon translation tasks and empirical results show
that bilingual sense embedding methods outperform existing bilingual word
embedding methods.

本研究提出了一种基于点互信息图的最大完整子图（Bilingual Sense Clique, BSC）从而将源语言和目标语言的词汇平等地处理，不需要分别使用双语投影处理，采用几种降维方法对 BSC - 词汇关系进行总结。经实验证明，该方法优于现有的双语词嵌入方法在双语词汇翻译任务上的效果。