STRUKTUR KALIMAT TUNGGAL BAHASA KANUM SOTA THE STRUCTURE OF THE SIMPLE SENTENCE OF KANUM SOTA LANGUAGE

Kanum Sota language is spoken by speaker aroun Sota District, Merauke, Papua Province. This study uses descriptive method to describe the structure of the simple sentence of Kanum Sota language. The result of analysis of the function of simple sentence elements shows us that the structures of simple sentence of Kanum Sota are Subject + Predicate (S+P), Subject + Object + Predicate (S + O + P), Subject + Predicate + Object (S + P + O), Subject + Adverb + Predicate (S + Adv. + P), and Subject + Obejct + Predicate + Adverb (S + O + P + Adv.). By simple sentence pattern, structure and kind of simple sentence of the Kanum Sota is known as follows: (1) transitive sentence whose pattern S:n/pron./NP + O:n/pron./NP + P: trans.Verb, and S:n/pron/NP + P:trans.verb + O:n/pron/NP, (2) Intransitive sentence pattern, S:n/pron/NP + P: intrans.verb, (3) descriptive sentence, S:n/pron/NP + P: adj./adj.phrase, (4) postposition sentence, S:n/pron/NP + P:postpositional phrase, (5) possessive sentence S:n/NP + P:possessive, (6) equative sentence S:n/pron/NP + P:n, and (7) numeral sentence S:n/pron/NP + P:num. Keywords: function, category, structure, simple sentence Abstrak Bahasa Kanum Sota digunakan oleh penutur yang tinggal di Distrik Sota, Kabupaten Merauke, Provinsi Papua. Penelitian ini menggunakana metode deskriptif yang bertujuan untuk mendeskripsikan struktur kalimat tunggal bahasa Kanum Sota. Berdasarkan hasil analisis fungsi unsur-unsur pembentuk kalimat tunggal, dapat diketahui struktur kalimat tunggal bahasa Kanum Sota, yaitu Subjek + Predikat (S + P), Subjek + Objek + Predikat (S + O + P), Subjek + Predikat + Objek (S + P + O), Subjek + Keterangan + Predikat (S + K + P), dan Subjek + Objek + Predikat + Keterangan (S + O + P + K). Berdasarkan pola dasar kalimat tunggal, dapat diketahui jenis kalimat dan struktur kalimat tunggal bahasa Kanum Sota, yaitu kalimat transitif berpola S:n/pron/fr.n + O:n/pron/fr.n + P:v.transitif terdapat juga kalimat transitif yang berpola S:n/pron/fr.n + P:v.transitif + O:n/pron/fr.n, kalimat intransitif berpola S:n/pron/fr.n + P:v intransitif, kalimat deskriptif berpola S:n/pron/fr.n + P:adj/fr.adj, kalimat posposisional berpola S:n/pron/fr.n + P:fr.posp, kalimat posesif berpola S:n/fr.n + P:Posf, kalimat ekuatif berpola S:n/pron/fr.n + P:n, dan kalimat numeralia berpola S:n/pron/fr.n + P:num. Kata kunci: fungsi, kategori, struktur, kalimat tunggal

Early linguistic experience shapes bilingual adults’ hearing for phonemes in both languages

How do bilinguals represent category structure for phonemes in early acquired languages? /b/ and /p/ voice onset times (VOTs) are earlier and closer together in English than in Mandarin. Hence the category boundary and the steepness of the transition can show ‘tuning’ to language-specific acoustic structure (i.e., earlier boundary and steeper slope for English). As preregistered, we mapped identification functions of early English-Mandarin bilingual adults (N=66) on a /b/ - /p/ VOT continuum in each language. Individual bilingual-balance was derived using principal component analysis and entered into GLMMs of categorical boundary and slope. As predicted, VOTs were earlier for English than Mandarin. Early bilingual-balance predicted the slope of the transition between categories: Those who heard more English from an earlier age showed steeper slopes than those who heard more Mandarin. Findings are consistent with discrete representations for each language, and transfer of category structure from the earlier acquired language.

People are Insensitive to Within-Category Feature Correlations in Categorization

This work compares two types of psychological similarity in categorization. Similarity is a central component of categorization theories. Exemplar theories, for instance, assume that people categorize new exemplars based on their similarity to previous category members. Traditionally, the underlying psychological similarity is based on the sum of two exemplars' squared feature value differences (Euclidean similarity). The Euclidean similarity, however, ignores the distribution of exemplars within categories by assuming uncorrelated features within categories. The Mahalanobis similarity, in turn, extends the Euclidean similarity by accounting for within-category feature correlations. Results from machine learning have shown that in categorization problems involving correlated features within categories, the Mahalanobis similarity can outperform the Euclidean similarity. On the empirical side, results from psychology indicate that people can be sensitive to within-category feature correlations: Some findings suggest a general sensitivity for within-category feature correlations, yet others have argued that this sensitivity depends on the category structure, task format, and amount of training. The present work rigorously tested the correlation-insensitive Euclidean similarity against the correlation-sensitive Mahalanobis similarity to investigate if people use within-category feature correlations in categorization.

SOME SPECIFIC FEATURES OF CONCEPTUAL CATEGORY ORGANIZATION (BASED ON RUSSIAN)

This article describes the notions of conceptual category, categorical and subcategorical concepts. The purpose of the work is to provide the rationale for applying the term “categorical concept”. We also offer the method for designing conceptual category structure and identifying its elements. The key role for the method is assigned to categorical concept features. To exemplify that, we performed lexicographical analysis of the categorical concept FAMILY in Russian linguaculture. We carried out the analysis using etymological, associative, definition dictionaries and dictionaries of synonyms. The study showed some peculiarities of a conceptual category structure. Particularly, it was demonstrated that not all features of subcategorical concepts correspond to the categorical concept, at the same time, some elements of a category can be assigned to different categorical groups. The analysis showed a number of concept FAMILY features: «association of people», «home», «unity», «size», «children», «belonging to someone», «strength of relationship», etc...

Novices Perform Like Experts on a Closed Card Sort but Not an Open Card Sort

Developing good psychological measures benefits from the input of content experts. For many constructs or domains, however, who constitutes an ‘expert’ might be ill-defined. Novices—such as students, customers, or co-workers—may possess the same knowledge as experts. Moreover, as convenience samples, novices are more readily available and less costly. Card sorting is a technique frequently used in human factors to elicit expert knowledge. This study compared novice and expert performance on a card sort task under two conditions, an open sort and a closed sort. Because the closed sort offered a category structure for sorting, it was predicted and found that novices in the closed sort tended to match expert sorting results more closely than in the open sort. The structure also made novice solutions better approximate the expert open sort. This suggests that novices can be useful in the follow-up stage of measure development, but not in the initial stage.

Abstract Concept Instantiation with Context Relevance Measurement

In different contexts, one abstract concept (e.g., fruit) may be mapped into different concrete instance sets, which is called abstract concept instantiation. It has been widely applied in many applications, such as web search, intelligent recommendation, etc. However, in most abstract concept instantiation models have the following problems: (1) the neglect of incorrect label and label incompleteness in the category structure on which instance selection relies; (2) the subjective design of instance profile for calculating the relevance between instance and contextual constraint. The above problems lead to false prediction in terms of abstract concept instantiation. To tackle these problems, we proposed a novel model to instantiate the abstract concept. Firstly, to alleviate the incorrect label and remedy label incompleteness in the category structure, an improved random-walk algorithm is proposed, called InstanceRank, which not only utilize the category information, but it also exploits the association information to infer the right instances of an abstract concept. Secondly, for better measuring the relevance between instances and contextual constraint, we learn the proper instance profile from different granularity ones. They are designed based on the surrounding text of the instance. Finally, noise reduction and instance filtering are introduced to further enhance the model performance. Experiments on Chinese food abstract concept set show that the proposed model can effectively reduce false positive and false negative of instantiation results.