Computational Models of the Emergence and Diachronic Change of Multi-Word Expression Meanings

在自然语言处理（NLP）中，如果单词的组合在某种程度上是语义特异性的，则它们被认为是多词表达（MWE），即，组合的含义不能完全（或甚至根本不能）从成分的含义中预测。MWE从属于多种形态句法类型，包括名词复合词（如“跳蚤市场”）和小品词动词（如“放弃”）。它们已经从共时的角度进行了广泛的跨学科研究，但最新的研究缺乏对MWE意义历时模型的实证性大规模方法。我们的项目SemChangeMWE超越了MWE意义的限制性共时概念，提供了一个新的视角来研究MWE的出现，MWE意义的变化和MWE的组合性（即，意思是透明性）通过计算建模它们的历时性质和性质的变化。我们选择了两个多方面的MWE类型名词复合词和粒子动词，以探讨他们跨语言的德语和英语。该项目汇集了我们在以下方面的专业知识：（a）MWE组合性和意义类比的计算模型，（B）语言变异中历时意义变化和意义分歧的计算模型，以及（c）意义成分和意义相关性的数据集，以解决跨学科缺乏MWE意义的计算历时模型。我们将采用定性和定量的方法，（如生产力的统计计量;分布、信息理论和基于主题和图表的概率模型;搭配强度的可视化），并增强向量表示和计算算法，以阐明（i）共时性突出的经验特点的MWE出现时（如频率，一般性，语法变化），（二）历时MWE意义的变化，（三）共时性和历时性的作用，在MWE意义的创新和减少，和（四）类比发展的MWE意义方面，其目前的组合。为了使理论和计算研究能够进行广泛的跨学科评估和验证，我们将不仅在一般语义变化基准和MWE特定的新变化数据集上评估我们的经验知识和计算模型，而且（i）通过验证它们对MWE的理论驱动分类;（ii）通过将它们应用于进一步的语言变异任务（即，域/寄存器和方言特定的意义分歧），以及（iii）通过将它们集成到统计机器翻译中作为外部NLP应用程序。

In Natural Language Processing (NLP), combinations of words are considered multi-word expressions (MWEs) if they are semantically idiosyncratic to some degree, i.e., the meaning of the combination is not entirely (or even not at all) predictable from the meanings of the constituents. MWEs subsume multiple morpho-syntactic types, including noun compounds (such as "flea market") and particle verbs (such as "give up"). They have been explored extensively and across research disciplines from synchronic perspectives, but state-of-the-art studies are lacking empirical large-scale approaches towards diachronic models of MWE meaning.Our project SemChangeMWE goes beyond the restricted synchronic concept of MWE meaning and provides a novel perspective on MWE emergence, MWE meaning changes and MWE compositionality (i.e., meaning transparency) by computationally modelling their diachronic properties and changes of properties. We selected the two multifaceted MWE types noun compounds and particle verbs to explore them cross-linguistically for German and English. The project brings together our expertises in (a) computational models of MWE compositionality and meaning analogy, (b) computational models of diachronic meaning changes and meaning divergences in language variation, and (c) datasets of meaning components and meaning relatedness, in order to address the interdisciplinary lack of computational diachronic models of MWE meaning.Methodologically, we will exploit qualitative and quantitative approaches (such as statistical measures of productivity; distributional, information-theoretic and topic- and graph-based probabilistic models; visualisation of collocational strength) and enhance vector representations and computational algorithms to shed light on (i) synchronically salient empirical characteristics of MWEs at the time of emergence (such as frequency, generality, grammatical variation), (ii) diachronic MWE meaning changes, (iii) the role of synchronic and diachronic polysemy in MWE sense innovation and reduction, and (iv) analogical developments of MWE meanings with regard to their present-day compositionality. To enable extensive interdisciplinary assessment and validation for theoretical and computational research, we will evaluate our empirical knowledge and computational models not only on general semantic-change benchmarks and MWE-specific novel change datasets, but also (i) by validating them against theory-driven categorisations of MWEs; (ii) by applying them to further language variation tasks (i.e., domain-/register- and dialect-specific sense divergences), and (iii) by integrating them into statistical machine translation as an external NLP application.