本文对区分语义抽象程度的分布度量进行了深入研究。抽象被认为是“认知科学的核心结构”(Barsalou,2003)和“允许有效存储和检索中心知识的信息减少过程”(Burgoon等人。,2013).依靠分配假设,计算研究已经成功地利用了上下文共生和邻域密度的度量来区分概念语义分类。到目前为止,这些研究对词汇语义任务的语义抽象进行了建模,例如歧义;历时意义变化;抽象性与具体性;和高度。然而,分布方法针对不同概念类型的语义相关性,至于我们的知识,没有太多注意应用,比较或分析跨概念任务的计算抽象度量。本文提出了一种新颖的观点,该观点利用分布度量的变体来根据抽象-具体的二分法研究英语中的语义抽象(例如,荣耀-香蕉)和一般性-特异性区分(例如,动物-鱼),为了比较抽象分类措施的优缺点,并确定和调查概念上的差异。在一系列实验中,我们为词汇语义抽象的实例化确定了可靠的分布度量,并达到了高于0.7的精度,但是对于抽象具体与抽象特定的区别和名词与动词。总的来说,我们确定了两组措施,(i)频率和单词熵,当区分或多或少的抽象单词时,和(ii)邻域密度变体(尤其是目标上下文多样性),当根据抽象-具体二分法区分或多或少的抽象单词时。我们得出的结论是,与更具体的单词相比,更一般的单词被更频繁地使用并且不那么令人惊讶,抽象词在语义上比具体词更多样化的上下文中凭经验建立自己。最后,我们的实验再次指出,概念分类的分布模型需要考虑词类和歧义:名词的结果与动词在许多方面不同,和模糊性阻碍了微调经验观察。
This article provides an in-depth study of distributional measures for distinguishing between degrees of semantic abstraction. Abstraction is considered a \"central construct in cognitive science\" (Barsalou, 2003) and a \"process of information reduction that allows for efficient storage and retrieval of central knowledge\" (Burgoon et al., 2013). Relying on the distributional hypothesis, computational studies have successfully exploited measures of contextual co-occurrence and neighbourhood density to distinguish between conceptual semantic categorisations. So far, these studies have modeled semantic abstraction across lexical-semantic tasks such as ambiguity; diachronic meaning changes;
abstractness vs. concreteness; and hypernymy. Yet, the distributional approaches target different conceptual types of semantic relatedness, and as to our knowledge not much attention has been paid to apply, compare or analyse the computational abstraction measures across conceptual tasks. The current article suggests a novel perspective that exploits variants of distributional measures to investigate semantic abstraction in English in terms of the abstract-concrete dichotomy (e.g., glory-banana) and in terms of the generality-specificity distinction (e.g., animal-fish), in order to compare the strengths and weaknesses of the measures regarding categorisations of abstraction, and to determine and investigate conceptual differences. In a series of experiments we identify reliable distributional measures for both instantiations of lexical-semantic abstraction and reach a precision higher than 0.7, but the measures clearly differ for the abstract-concrete vs. abstract-specific distinctions and for nouns vs. verbs. Overall, we identify two groups of measures, (i) frequency and word entropy when distinguishing between more and less abstract words in terms of the generality-specificity distinction, and (ii) neighbourhood density variants (especially target-context diversity) when distinguishing between more and less abstract words in terms of the abstract-concrete dichotomy. We conclude that more general words are used more often and are less surprising than more specific words, and that abstract words establish themselves empirically in semantically more diverse contexts than concrete words. Finally, our experiments once more point out that distributional models of conceptual categorisations need to take word classes and ambiguity into account: results for nouns vs. verbs differ in many respects, and ambiguity hinders fine-tuning empirical observations.