Semantic compositionality (SC) is defined as the linguistic phenomenon that the meaning of a syntactically complex unit is a function of meanings of the complex unit’s constituents and their combination rule (Pelletier1994). Some linguists regard SC as the fundamental truth of semantics (Pelletier2016). In the field of NLP, SC has proved effective in many tasks including language modeling (Mitchell2009)maas2011learning; Socher2013), syntactic parsing (socher2013parsing), etc.
Most literature on SC pays attention to using vector-based distributional models of semantics to learn representations of multiword expressions (MWEs), i.e., embeddings of phrases or compounds.Mitchell2008 conduct a pioneering work in which they introduce a general framework to formulate this task:
where is the compositionality function, p denotes the embedding of an MWE, and represent the embeddings of the MWE’s two constituents, stands for the combination rule and refers to the additional knowledge which is needed to construct the semantics of the MWE.
Among the proposed approaches for this task, most of them ignore and , centering on reforming compositionality function (baroni2010nouns; grefenstette2011experimental; Socher2012; Socher2013). Some try to integrate combination rule into SC models (Blacoe2012; Zhao2015; Weir2016; Kober2016). Few works consider external knowledge . Zhu2016 try to incorporate task-specific knowledge into an LSTM model for sentence-level SC. As far as we know, however, no previous work attempts to use general knowledge in modeling SC.
|SCD||Our Computation Formulae||Examples|
|MWEs and Constituents||Sememes|
|几何 (geometry; how much)||数学math,知识knowledge,疑问question,功能词funcword|
应考(engage a test)考试exam,从事engage
应 (deal with; echo; agree)处理handle,回应respond,同意agree,遵循obey,功能词funcword,姓surname
画 (draw)画draw,部件part,图像image, 文字character,表示express