We introduce temporally and contextually-aware models for the novel task of
predicting unseen but plausible concepts, as conveyed by noun-noun compounds in
a time-stamped corpus. We train compositional models on observed compounds,
more specifically the composed distributed representations of their
constituents across a time-stamped corpus, while giving it corrupted instances
(where head or modifier are replaced by a random constituent) as negative
evidence. The model captures generalisations over this data and learns what
combinations give rise to plausible compounds and which ones do not. After
training, we query the model for the plausibility of automatically generated
novel combinations and verify whether the classifications are accurate. For our
best model, we find that in around 85% of the cases, the novel compounds
generated are attested in previously unseen data. An additional estimated 5%
are plausible despite not being attested in the recent corpus, based on
judgments from independent human raters.

本文介绍了一种感知时间和语境的模型用于预测未知合成名词所表达的可信概念，该模型在在一个时间戳语料库上训练，可以识别真实的和虚假的合成名词。经测试，该模型在多大约 85% 的情况下能正确分类自动生成的新的合成名词并有五分之一的新合成名词被评定为可信。