Despite the incremental nature of Dynamic Syntax (DS), the semantic grounding of it remains that of predicate logic, itself grounded in set theory, so is poorly suited to expressing the rampantly context-relative nature of word meaning, and related phenomena such as incremental judgements of similarity needed for the modelling of disambiguation. Here, we show how DS can be assigned a compositional distributional semantics which enables such judgements and makes it possible to incrementally disambiguate language constructs using vector space semantics. Building on a proposal in our previous work, we implement and evaluate our model on real data, showing that it outperforms a commonly used additive baseline. In conclusion, we argue that these results set the ground for an account of the non-determinism of lexical content, in which the nature of word meaning is its dependence on surrounding context for its construal.

尽管动态语法 (DS) 具有增量性质，但它的语义基础仍然是谓词逻辑的语义基础，本身以集合论为基础，因此不太适合表达词义的猖獗的上下文相关性质，以及相关现象，如增量消歧建模所需的相似性判断。在这里，我们展示了如何为 DS 分配组合分布语义，从而实现此类判断，并使使用向量空间语义逐步消除语言结构的歧义成为可能。基于我们之前工作中的建议，我们在真实数据上实施和评估我们的模型，表明它优于常用的加法基线。总之，我们认为这些结果为解释词汇内容的非确定性奠定了基础，