CAOSS and Transcendence – On the Representation and Processing of Noun Compounds

名词复合词，如keyposition，由两个成分组成：中心名词（这里是position）和修饰语（这里是key）。然而，根据形态超越假说，这些成分的意义并不直接在名词复合词的心理表征和认知加工中发挥作用。人们更倾向于认为，这些成分根据它们在复合词中的位置（修饰语或中心语），在更常用的情况下，发展出了改变的、位置上受约束的表征。例如，通过考虑关键位置或关键时刻等情况，很明显，如果用作修饰语，key-的含义与key的自由词含义不同。形态超越假说认为这些位置上的表征在复合词的表征或加工中起着重要的作用。 对于非常流行的修饰语，如德语的奥普特-（头-），甚至可以观察到，这些修饰语实际上失去了它们的自由词的意义，并发挥了类似词缀的作用。这可以被认为是修饰语表征的进一步变化和超越，超越了位置约束表征而进入词缀。使用分布语义模型的组合方法，这些不同的表征可以直接量化。在分布式语义模型中，词义被概念化为高维数值向量。这些向量是从大型自然语言语料库中的单词分布中获得的。使用组合模型，例如CAOSS模型，名词复合词的向量表示可以从其成分的向量表示计算。至关重要的是，在CAOSS模型中，作为中间步骤，计算组分的位置约束矢量表示，然后将其相加以获得复合矢量表示。有了这些位置约束的矢量表示的成分在手，一系列重要的预测，从形态超越假设可以在一系列的计算和实验研究中进行经验检验，详细描述在项目说明。另一个组成模型，功能的方法，将修改器概念化为应用于头部向量表示以计算复合向量表示的线性函数。这种函数方法可以成功地用作表示附加词的模型（例如，通过将用于mis-的线性函数应用于用于fortune的向量表示来计算不幸的含义）。在这里提出的项目的第二部分，一系列的计算和实验研究计划，以检查是否这样的功能的方法是一个更充分的模型，以获得复合的意义作为修饰语更频繁地使用，并开发一个词缀样的表示。

Noun compounds, such as key position, consist of two constituents: the head noun (here: position) and the modifier (here: key). However, according to the morphological transcendence hypothesis, the meaning of these constituents does not directly play a role in the mental representation and cognitive processing of noun compounds. It is rather assumed that these constituent develop altered, positionally bound representations as they are more commonly used, depending on their position in the compound (modifier or head). By considering cases such as key position or key moment, for example, it is clear that the meaning of key-, if used as a modifier, differs from the free word meaning of key. The morphological transcendence hypothesis assumes that these positionally bound representations play an important role in the representation or processing of compounds. For highly prevalent modifiers, such as the German Haupt- (head-), it is even observed that these modifiers virtually lose their free word meanings and play an affix-like role. This can be considered as a further change and transcendence in the representation of the modifiers, beyond positionally bound representations and into affixes.Using compositional methods for models of distributional semantics, these different representations can be quantified directly. In distributional semantics models, word meanings are conceptualized as high-dimensional numerical vectors. These vectors are obtained from word distributions in large corpora of natural language. With compositional models, such as the CAOSS model, vector representations for noun compounds can be computed from the vector representations for their constituents. Crucially, in the CAOSS model, positionally bound vector representations for the constituents are computed as an intermediate step, which are then added up to obtain the compound vector representation. With these positionally bound vector representations for the constituents at hand, a series of important predictions that follow from the morphological transcendence hypothesis can be examined empirically in a series of computational and experimental studies, as described in detail in the Project Description.Another compositional model, the functional approach, conceptualizes the modifier as a linear function that is applied to the head vector representation to compute a compound vector representation. Such a functional approach can be employed successfully as a model for representing affixed words (for example, the meaning of misfortune is computed by applying the linear function for mis- to the vector representation for fortune). In the second part of the project proposed here, a series of computational and experimental studies is planned to examine whether such a functional approach is a more adequate model for obtaining compound meanings as a modifier is more frequently used and develops an affix-like representation.