The context in which a sentence appears can drastically alter our
expectations about upcoming words - for example, following a short story
involving an anthropomorphic peanut, experimental participants are more likely
to expect the sentence 'the peanut was in love' than 'the peanut was salted',
as indexed by N400 amplitude (Nieuwland & van Berkum, 2006). This rapid and
dynamic updating of comprehenders' expectations about the kind of events that a
peanut may take part in based on context has been explained using the construct
of Situation Models - updated mental representations of key elements of an
event under discussion, in this case, the peanut protagonist. However, recent
work showing that N400 amplitude can be predicted based on distributional
information alone raises the question whether situation models are in fact
necessary for the kinds of contextual effects observed in previous work. To
investigate this question, we attempt to model the results of Nieuwland and van
Berkum (2006) using six computational language models and three sets of word
vectors, none of which have explicit situation models or semantic grounding. We
find that the effect found by Nieuwland and van Berkum (2006) can be fully
modeled by two language models and two sets of word vectors, with others
showing a reduced effect. Thus, at least some processing effects normally
explained through situation models may not in fact require explicit situation
models.

通过计算语言模型和词向量，我们模拟了句子上下文对语义预期的影响，结果表明情境模型可能并不是解释上下文影响的唯一方式。