Optimal categorisation

The debate as to whether language influences cognition has been long standing but has yielded conflicting findings across domains such as colour and kinship categories. Fewer studies have investigated systems such as nominal classification (gender, classifiers) across different languages to examine the effects of linguistic categorisation on cognition. Effective categorisation needs to be informative to maximise communicative efficiency but also simple to minimise cognitive load. It therefore seems plausible to suggest that different systems of nominal classification have implications for the way speakers conceptualise relevant entities. A suite of seven experiments was designed to test this; here we focus on our card sorting experiment, which contains two sub-tasks — a free sort and a structured sort. Participants were 119 adults across six Oceanic languages from Vanuatu and New Caledonia, with classifier inventories ranging from two to 23. The results of the card sorting experiment reveal that classifiers appear to provide structure for cognition in tasks where they are explicit and salient. The free sort task did not incite categorisation through classifiers, arguably as it required subjective judgement, rather than explicit instruction. This was evident from our quantitative and qualitative analyses. Furthermore, the languages employing more extreme categorisation systems displayed smaller variation in comparison to more moderate systems. Thus, systems that are more informative or more rigid appear to be more efficient. The study implies that the influence of language on cognition may vary across languages, and that not all nominal classification systems employ this optimal trade-off between simplicity and informativeness. These novel data provide a new perspective on the origin and nature of nominal classification.

Expansion by migration and diffusion by contact is a source to the global diversity of linguistic nominal categorization systems

Languages of diverse structures and different families tend to share common patterns if they are spoken in geographic proximity. This convergence is often explained by horizontal diffusibility, which is typically ascribed to language contact. In such a scenario, speakers of two or more languages interact and influence each other’s languages, and in this interaction, more grammaticalized features tend to be more resistant to diffusion compared to features of more lexical content. An alternative explanation is vertical heritability: languages in proximity often share genealogical descent. Here, we suggest that the geographic distribution of features globally can be explained by two major pathways, which are generally not distinguished within quantitative typological models: feature diffusion and language expansion. The first pathway corresponds to the contact scenario described above, while the second occurs when speakers of genetically related languages migrate. We take the worldwide distribution of nominal classification systems (grammatical gender, noun class, and classifier) as a case study to show that more grammaticalized systems, such as gender, and less grammaticalized systems, such as classifiers, are almost equally widespread, but the former spread more by language expansion historically, whereas the latter spread more by feature diffusion. Our results indicate that quantitative models measuring the areal diffusibility and stability of linguistic features are likely to be affected by language expansion that occurs by historical coincidence. We anticipate that our findings will support studies of language diversity in a more sophisticated way, with relevance to other parts of language, such as phonology.

Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach

In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic crash injury severity and to test its performance over existing machine learning classification methods. First, we compare the performance of the neural network, XGBoost, and SVM classifiers in injury severity prediction. Second, we utilize a severity category-combination method with oversampling to relieve the class-imbalance problem prevalent in crash data. Third, we take advantage of probability calibration and the optimal probability threshold moving to improve the prediction ability of ordinal classification. The proposed approach can satisfy the rank consistency and rank monotonicity requirement and is proved to be superior to other ordinal classification methods and nominal classification machine learning by statistical significance test. Important factors relating to injury severity are selected based on their permutation feature importance scores. We find that converting severity levels into three classes, minor injury, moderate injury, and serious injury, can substantially improve the prediction precision.

Systèmes De Classification Nominale En Asie Du Sud-Est : Les Différentes Fonctions Des Morphèmes Classificatoires

This article focuses on classifiers, one system of the nominal classification domain which is found in Southeast Asian languages. One of the functions associated with classifiers is the categorization of the nominal lexicon according to the semantic characteristics of the referent. Unsurprisingly, classifiers in Southeast Asia are organized around the basic semantic domains of the different systems of nominal classification. Although the system of so-called ‘numeral’ classifiers, whose primary function is to quantify referents, is the best known and most widespread in Southeast Asia, classifiers can encode various functions according to the syntactic constructions in which they appear. In some languages, these morphemes compete with class terms, a second nominal classification system. Sometimes the same form may belong to several paradigms, thus recalling a well-known characteristic of South-East Asian languages: the polyfunctionalty of forms.

Noun Classes and Plurality in Bantu Languages

Noun classes are a prominent grammatical feature of Bantu languages where typically each noun (or noun stem) is assigned to one of between fifteen and eighteen noun classes. Noun classes are often analysed as a form of nominal classification system and seen as belonging to the same domain as grammatical gender systems. Number in Bantu languages is mediated by the noun class system and the intricate interaction between noun class and number in Bantu has given rise to different theoretical analyses. The chapter focuses on three approaches to analysing grammatical number in Bantu languages—approaches based on an inflectional notion of number, those which analyse number as a derivational relation, and approaches adopting notions of polysemy and paradigms for analysing Bantu noun class systems.

The gender system of Durop

This paper presents an analysis of grammatical gender and agreement in Durop, a language of the Upper Cross subgroup of Cross River. The data used are drawn from Kastelein (Kastelein, Bianca. 1994. A phonological and grammatical sketch of DuRop. Leiden: University of Leiden Scriptie), whose analysis treats gender as the singular – plural pairings of nouns different from the present approach. Kastelein identifies eight concord classes (agreement classes); these form the basis of gender in Durop in the present analysis; as many as 24 agreement classes are identified here. The various systems comprising nominal classification, agreement and gender in Durop are compared and discussed. The agreement system comprises three subsystems of differing numbers of agreement classes.

The gender system of Longuda

This article describes the gender system of Longuda. Longuda class marking is alliterative and does not distinguish between nominal form and agreement marking. While it thus appears to be a prototypical example of a traditional Niger-Congo “noun-class” system, this identity of gender encoding makes it look morpho-syntactic rather than lexical. This points to a formerly independent status of the exponents of nominal classification, which is similar to a classifier system and thus less canonical. Both types of class marking hosts involve two formally and functionally differing allomorphs, which inform the historical reconstruction of Longuda noun classification in various ways.

More diversity enGENDERed by African languages: an introduction

We give an overview of current research questions pursued in connection with an ongoing project on nominal classification systems in Africa, with a particular focus on Niger-Congo. We first introduce our cross-linguistically applicable methodological approach which provides new insights into the design of a range of gender systems on the continent. We then apply these ideas to the “noun class” systems of Niger-Congo. We focus on non-canonical phenomena of poorly known languages, which attest to an unexpected systemic diversity beyond the well-known Bantu type and promise to change the synchronic and diachronic perspective on the gender systems of this family.