  Semantic compositionality (SC) refers to the phenomenon that the meaning of a
complex linguistic unit can be composed of the meanings of its constituents.
Most related works focus on using complicated compositionality functions to
model SC while few works consider external knowledge in models. In this paper,
we verify the effectiveness of sememes, the minimum semantic units of human
languages, in modeling SC by a confirmatory experiment. Furthermore, we make
the first attempt to incorporate sememe knowledge into SC models, and employ
the sememeincorporated models in learning representations of multiword
expressions, a typical task of SC. In experiments, we implement our models by
incorporating knowledge from a famous sememe knowledge base HowNet and perform
both intrinsic and extrinsic evaluations. Experimental results show that our
models achieve significant performance boost as compared to the baseline
methods without considering sememe knowledge. We further conduct quantitative
analysis and case studies to demonstrate the effectiveness of applying sememe
knowledge in modeling SC. All the code and data of this paper can be obtained
on https://github.com/thunlp/Sememe-SC.

Computation and Language