When the meaning of a phrase cannot be inferred from the individual meanings
of its words (e.g., hot dog), that phrase is said to be non-compositional.
Automatic compositionality detection in multi-word phrases is critical in any
application of semantic processing, such as search engines; failing to detect
non-compositional phrases can hurt system effectiveness notably. Existing
research treats phrases as either compositional or non-compositional in a
deterministic manner. In this paper, we operationalize the viewpoint that
compositionality is contextual rather than deterministic, i.e., that whether a
phrase is compositional or non-compositional depends on its context. For
example, the phrase `green card' is compositional when referring to a green
colored card, whereas it is non-compositional when meaning permanent residence
authorization. We address the challenge of detecting this type of contextual
compositionality as follows: given a multi-word phrase, we enrich the word
embedding representing its semantics with evidence about its global context
(terms it often collocates with) as well as its local context (narratives where
that phrase is used, which we call usage scenarios). We further extend this
representation with information extracted from external knowledge bases. The
resulting representation incorporates both localized context and more general
usage of the phrase and allows to detect its compositionality in a
non-deterministic and contextual way. Empirical evaluation of our model on a
dataset of phrase compositionality, manually collected by crowdsourcing
contextual compositionality assessments, shows that our model outperforms
state-of-the-art baselines notably on detecting phrase compositionality.

本文提出了一种基于 contextual word embedding 以及外部知识库的方法，用于实现多词组的非固定组合性自动检测，该方法在数据集测评中表现良好。