Distributional Approaches to Semantic Relatedness: Generalisation, Evaluation, Visualisation

该项目在理论语言学、认知语言学和计算语言学之间的跨学科框架内探索了词汇语义学的分布方法的潜力和局限性。我们依靠三个测试用例来区分不同类型的语义关联，汇集了项目第一阶段的任务、方法和结果，并从元水平的角度研究语义关联：我们将语义关联作为一个通用术语，包括聚合关系对、复合成分对和跨词语类型的语义关联，并计划(I)调查这些类型的语义关联、跨词类和跨语言的分布方法；(Ii)探索软聚类方法、参数和评估的语言性能来建模歧义；(Iii)利用可视化作为评估分布空间中语义关联的工具；以及(Iv)作为外部应用的统计机器翻译的语义子范畴信息和语义评估的抽象。在不同的任务和观点中，分配办法继续解决两大挑战：(1)在理论上和认知上充分选择特征，以模拟词义和词的关联性；(2)根据词义模拟词义，以解决歧义问题。

The project explores the potential and the limits of distributional approaches to lexical semantics within an interdisciplinary framework between theoretical, cognitive and computational linguistics. Relying on three test cases to distinguish various types of semantic relatedness, we bring together the tasks, approaches and results from the first project phase and study semantic relatedness from a meta-level perspective: We take "semantic relatedness" as a generic term subsuming paradigmatic relation pairs, compound-constituent pairs and sense relatedness across word types, and plan to (i) investigate distributional approaches across these types of semantic relatedness, across word classes, and across languages; (ii) explore the linguistic performance of soft clustering approaches, parameters, and evaluations to model ambiguity; (iii) exploit visualisation as a tool to assess semantic relatedness in distributional spaces; and (iv) abstract over semantic subcategorisation information and semantic evaluation for statistical machine translation as an extrinsic application. Across tasks and views, the distributional approaches continue to address two major challenges, (i) a theoretically and cognitively adequate selection of features to model word meaning and word relatedness, and (ii) modelling word meaning with respect to word senses, to address ambiguity.