Modelling compositional meaning for sentences using empirical distributional
methods has been a challenge for computational linguists. We implement the
abstract categorical model of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) using
data from the BNC and evaluate it. The implementation is based on unsupervised
learning of matrices for relational words and applying them to the vectors of
their arguments. The evaluation is based on the word disambiguation task
developed by Mitchell and Lapata (2008) for intransitive sentences, and on a
similar new experiment designed for transitive sentences. Our model matches the
results of its competitors in the first experiment, and betters them in the
second. The general improvement in results with increase in syntactic
complexity showcases the compositional power of our model.

利用经验分配法对句子进行组合意义建模一直是计算语言学家面临的挑战。我们利用 BNC 数据实现了 Coecke 等人的抽象范畴模型，并进行了评估。实现基于无监督学习的关系词矩阵，应用于其参数的向量。我们的模型在失歧任务上拥有比竞争对手更优异的表现，而随着句法复杂度的增加，结果的普遍改善展示了我们模型的组合能力。