MUDCAT - MUltimodal Dimensions and Computational Applications of AbstracTness

抽象词和具体词之间的区别（例如“dream”与“banana”的对比）代表了与自然语言处理（NLP）目的高度相关的语义分类。在这种情况下，我们的项目MUDCAT从数据驱动和面向应用程序的角度研究了抽象性的概念。虽然关于抽象性的讨论已经在认知科学中进行了很长时间，但我们解决并加强了现有定义，数据收集和表征中的关键问题，并扩大和优化了NLP方法中有效利用的视角。迄今为止，抽象性的定义，收集和应用大多是在没有上下文的情况下在单词类型的基础上进行的。相比之下，MUDCAT将开发，利用和应用抽象性的经验维度，同时注意跨词类（名词，动词，形容词），跨模态（文本，联想，特征，图像）和跨语言（英语，德语，意大利语）的基于标记的，与意义相关的视角。在这种情况下，我们将收集新的人类生成的规范的抽象性，并利用跨语言的转移，以推进规范生成的半自动算法。一个主要的努力，在经验层将识别和诱导词类依赖的显着尺寸的抽象从大规模语料库，考虑到上下文条件的形式的句法星座（如子分类和修改）。考虑到抽象性在概念上不同于多模态的具体性，我们将超越文本维度，收集和探索抽象性在自由词联想、特征属性生成和图像中的多模态方面。基于类和跨语言的聚类方法将调查多模态特征的语义和语言概括。最后，多模态跨语言的经验知识的抽象性将被应用到NLP任务的性能是已知的或预期受益于抽象性知识。因此，我们将开发通用的计算方法来应用我们增强的抽象信息的语义挑战：比喻语言识别作为具体的抽象映射任务，和上位词检测作为语义的一般性任务。总体而言，MUDCAT将调查跨语言的可转移性的定义和应用的抽象和具体的话，英语，德语和意大利语，同时考虑到目标和上下文的歧义。

The distinction between abstract and concrete words (such as "dream" in contrast to "banana") represents a semantic categorisation highly relevant for Natural Language Processing (NLP) purposes. In this vein, our project MUDCAT investigates the notion of abstractness from a data-driven and application-oriented point of view. While the most long-standing discussions about abstractness have taken place in the cognitive sciences, we address and enhance critical issues in existing definitions, data collections and characterisations, and broaden and optimise the perspective towards effective exploitation in NLP approaches.Up to date, definitions, collections and applications of abstractness have mostly been performed on a word-type basis without contextualisation. In contrast, MUDCAT will develop, exploit and apply empirical dimensions of abstractness while paying attention to a token-based, sense-related perspective across word classes (nouns, verbs, adjectives), across modalities (text, associations, features, images) and across languages (English, German, Italian). In this vein, we will collect novel human-generated norms on abstractness and exploit cross-lingual transfer to advance semi-automatic algorithms for norm generation. A major effort at the empirical layer will identify and induce word-class-dependent salient dimensions of abstractness from large-scale corpora, taking into account contextual conditions in the form of syntactic constellations (such as subcategorisation and modification). Considering that abstractness is conceptually distinguished from concreteness on multimodal grounds, we will go beyond the textual dimension and collect and explore multimodal facets of abstractness in free word associations, feature-property generation and images. Class-based and cross-lingual clustering approaches will investigate semantic and language generalisations of the multimodal characteristics. Finally, the multimodal cross-lingual empirical knowledge of abstractness will be applied to NLP tasks whose performance is known or expected to profit from abstractness knowledge. Accordingly, we will develop generic computational approaches to apply our enhanced abstractness information to semantic challenges: figurative language identification as concrete-abstract mapping task, and hypernymy detection as semantic generality task. Overall, MUDCAT will investigate the cross-lingual transferability in definitions and applications of abstract and concrete words for English, German and Italian, while taking ambiguity of targets and contexts into account.