Distributional Approaches to Semantic Relatedness: Generalisation, Evaluation, Visualisation

该项目探讨了在理论，认知和计算语言学之间的跨学科框架内词汇语义分布方法的潜力和局限性。依靠三个测试案例来区分各种类型的语义相关性，我们将项目第一阶段的任务，方法和结果结合在一起，并从元层面的角度研究语义相关性：我们把“语义相关性”作为一个范畴，包括聚合关系对、复合成分对和跨词类的意义相关性，并计划（i）调查这些类型的语义相关性，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，以模拟歧义;（iii）利用可视化作为工具来评估分布空间中的语义相关性;以及（iv）抽象语义子分类信息和语义评估，以用于统计机器翻译作为外部应用。在任务和视图中，分布方法继续解决两个主要挑战，（i）在理论上和认知上充分选择特征来建模词义和词相关性，以及（ii）根据词义建模词义，以解决歧义。

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.