Locative Existential Sentences in Kyrgyz and Turkish Languages

This research aimed to find out the most frequent types of inflectional suffixes, derivational suffixes, noun phrases and verb phrases produced by the Postgraduate Students in writing argumentative text. This research used Descriptive Quantitative Research. The data were obtained by using writing test. The samples of this research were 16 Postgraduate Students of English Education Master Program in Academic Year 2023 selected by using Total Sampling Technique. The results of data analysis from 16 samples found that 1) The inflectional suffixes of plurality {-s} were 46,06%, 3rd person singular {s/es} were 8.33%, past tense/passive {-ed} were 14.58%, participle {-ing} were 17.59 %, possessive {-s} were 3,93%, and comparative/ superlative degrees {er/est} were 3.47%. 2) The derivational suffixes forming noun were 52.4%, forming verb were 3.2%, forming adjective were 23.92%, and forming adverb were 19.28%. 3) The constructions of simple noun phrases were 18.86%, rather complex noun phrases were 23.58 %, and very complex noun phrases were 56.83%. 4) The constructions of simple verb phrases were 82.5% and rather complex verb phrases were 16.87%. Thus, it can be concluded that the most frequent types of inflectional suffixes used by the students were plurality {-s}, the type of derivational suffixes was noun formation, the type of noun phrases was very complex noun phrases, and the type of verb phrases was simple verb phrases.

Noun phrase usage and elaboration is an important feature of literate language. In this study, we described noun phrase usage and elaboration in the narrative writing samples of intermediate grade students with and without language-based learning disabilities. Narrative writing samples from 64 students in fourth through sixth grades were coded for five types of noun phrases using coding procedures adapted from prior research. Noun phrase ratios (NPR) were calculated for each type of noun phrase evaluated in the study. NPRs represented the proportion of noun phrases to total clauses produced in the sample. Students in this study included all five types of noun phrases in their narrative writing to varying degrees. Between-group differences were observed in the frequency of complex noun phrase usage. Significant relationships between NPRs, analytic writing measures, and a standardized reading measure were observed. Noun phrase usage is an important consideration for both theoretical and clinical purposes. Findings from this study are related to theoretical models of writing and levels of language frameworks. The clinical relevance for noun phrase assessment and intervention for intermediate grade students with language-based learning disability is discussed.

Urdu language is a member of Indo-European family tree and within the zone of Indo-Iranian branch, whereas Turkish language is a member of Altaic family tree. Both of these languages belong to different family trees, but these languages have many words in common. Urdu language has 41 consonant sounds and 11 vowel sounds, whereas Turkish language has 21 consonant sounds and 8 vowel sounds. Both of these languages don’t have same number of phonemes. It is interesting to contemplate, how the speakers of both languages produce and perceive these common words in their languages. Therefore, a study is designed to explore the phonemic variations in similar words of Urdu and Turkish languages. In order to find out variations, a list of 75 words, which are common in Urdu and Turkish language was prepared in form of text. The data of this study was collected from Pak-Turk school of Lahore. The prepared words list was given to 10 Turkish speakers. These speakers were selected by using purposive sampling technique. The audios of the speakers were recorded and transcribed into standard Turkish IPA symbols. Levenshtein algorithm framework is used to draw a comparative analysis of Turkish phonemic transcriptions with standard Urdu phonemic transcriptions of the same words. With the help of Levenshtein algorithm, phonemic variations in similar words of both languages was measured. The intriguing result of the study will help in establishing the understanding about the production directions of the similar words used by the speakers of both languages.

This paper focuses on the capacity of the case markers in the Mongolian language, as a relative element, to generate any finite noun phrase or relative clause based on their syntactic function or relationship. In Mongolian, there are two different approaches to generate noun phrases: parataxis and hypotaxis. According to my early observation, if the noun phrase generated through the parataxis, is the complement of the postpositional phrase, the head word of the relevant noun phrase can be truncated. In other words, since this head noun is governed by case marker in its null form to generate the postpositional phrase, the head noun can be encoded. The second approach generates two different types of noun phrases in their structures: free structured and non-free structured noun phrases. Of them, the free structured noun phrase allows any syntactic transformations in their internal structure based on the senses of the case markers which denote a relation. That is to say, the null constituents in this type of noun phrases can be encoded to generate an extended alternative of the noun phrase and a relative clause.

Grammars published during the last decades of the Ottoman Empire adopted a comparative method that emphasized the similarities between the Ottoman Turkish, Arabic and Persian languages. This is demonstrated by examples such as the Kavaid-i Osmaniyye (“Ottoman-Turkish Grammar”, 1851) of Ahmet Cevdet and Mehmet Fuat, and the Kavâid-i Lisân-i Türkî (“Grammar of the Ottoman-Turkish Language”, 1892) of Tahir Kenan. These texts also demonstrate that grammars published in this period used Arabic terminology and concepts from Arabic linguistics to describe the structure of the Ottoman Turkish language (e.g., sarf for “morphology” and nahiv for “syntax”). Later, the transition between the Late Ottoman and the Early Turkish Republican Eras was marked by the emergence of new trends in Turkish language studies. These included the definition of a theoretical framework that described Turkish with the same concepts and terminology used for the description of languages spoken in Europe. This paradigm shift brought about significant changes in the structure of Turkish language grammars. Manuals published from 1930 onwards, such as the Türk Dilbilgisi Dersleri (“Lessons of Turkish Grammar”, 1936) – by Ahmet Cevat Emre – and the Ana Hatları ile Türk Grameri (“Turkish Language Through its Main Features”, 1940) of Tahsin Banguoğlu use terms and categories from European linguistics, such as “morphology”, “phonetics”, “etymology”, “semantics” (often indicating their French translation in parentheses). In some cases, the authors of these books explicitly state that their approach to grammatical studies aims to describe the features of the Turkish language independently of those of Arabic and Persian. This study examines these grammars in order to retrace the evolution of the structure of grammars of the Turkish language. Furthermore, it seeks to outline the role of European linguistics in the systematization of Turkish linguistics as independent from Arabic and Persian linguistics.

This research is aimed at identifying the parts of speech for the Kazakh and Turkish languages in an information retrieval system. The proposed algorithms are based on machine learning techniques. In this paper, we consider the binary classification of words according to parts of speech. We decided to take the most popular machine learning algorithms. In this paper, the following approaches and well-known machine learning algorithms are studied and considered. We defined 7 dictionaries and tagged 135 million words in Kazakh and 9 dictionaries and 50 million words in the Turkish language. The main problem considered in the paper is to create algorithms for the execution of dictionaries of the so-called Link Grammar Parser (LGP) system, in particular for the Kazakh and Turkish languages, using machine learning techniques. The focus of the research is on the review and comparison of machine learning algorithms and methods that have accomplished results on various natural language processing tasks such as grammatical categories determination. For the operation of the LGP system, a dictionary is created in which a connector for each word is indicated – the type of connection that can be created using this word. The authors considered methods of filling in LGP dictionaries using machine learning. The complexities of natural language processing, however, do not exclude the possibility of identifying narrower tasks that can already be solved algorithmically: for example, determining parts of speech or splitting texts into logical groups. However, some features of natural languages significantly reduce the effectiveness of these solutions. Thus, taking into account all word forms for each word in the Kazakh and Turkish languages increases the complexity of text processing by an order of magnitude

The article draws attention to the role and scope of proverbs and sayings in Kazakh and Turkish languages, proverbs and sayings in two languages are analyzed for their correspondence to the meaning on the basis of actual linguistic data. The most frequently used Kazakh proverbs were selected and their equivalents in Turkish language were found and presented. They were compared according to their translation methods. Turkish and Kazakh languages are originated from the same language family: Turkic. It is also important to note that Kazakh and Turkish culture are quite similar and proverbs can be also alike. The article outlines the history of the study of Kazakh and Turkish proverbs and sayings, defines their function in representing folk culture and traditions, customs in proverbs and sayings. We have considered the proverbs of the Kazakh and Turkish languages and ways of their application. The peculiarities and differences in the cultures of the two peoples are discussed, and the factors influencing their semantics and structure are presented. We have distinguished that most Kazakh and Turkish proverbs retain their original, basic meaning. The main purpose of the study is to analyse Kazakh and Turkish proverbs, identify their similarities, specifics and identify the national and cultural characteristics of both languages. The scientific significance of the article lies in the fact that it makes a certain contribution to the comparative study of the proverbs of the Turkic peoples. Practical significance of the study: the results of the work can be used in the research work of students in the humanities.

This research is conducted to identify the noun phrase and its types in Carlo Collodi’s novel entitled Pinoccio as well as procedures used to translate them in the translation version entitled Pinokio which was translated by Wiwin Indiarti.
 The data of this descriptive qualitative research are noun phrases found in the novel and their translation in Indonesian. Documentation is used to collect the data. Meanwhile, content analysis method is applied for analyzing data in relation to their contexts.
 The result of this research shows that there are 5.283 noun phrases in chapter 1 to chapter 36 of Pinochio novel. It is also found that there are 3 types of noun phrase. They are: 1) noun phrase type I which is identified as Modifier + Head (M+H) with 2.985 data, 2) noun phrase type II which is identified as Head + Modifier (M+H) with 425 data, and 3) noun phrase type III which is identified as Modifier + Head + Modifier (M+H) with 1.873 data.
 Furthermore, the translator used eleven translation procedures to translate noun phrases proposed by Newmark. Those procedures are 1) translation by Transference with frequency of 93 data (1.8%), 2) translation by Naturalization with frequancy of 102 data (2.1%), 3) translation by Cultural Equivalent with frequency of 308 data (6.2%), 4) translation by Functional Equivalent with frequancy of 97 data (1.9%) translation by Componential Analysis with frequancy of 773 data (14.7%), 6) translation by Synonymy with frequancy of 738 data (14.9%), 7) translation by Through-translation with frequancy of 1.205 data (24.3%), 8) translation by Shift or Transposition with frequancy of 792 data (17%), 9) translation by Compensation with frequancy of 438 data (8%). 10 translation by Couplets with frequency 173 (3.4%), and 11) translation by Modulation with frequency 43 (1%). 
 It can be concluded that the translator often used Through translation procedure to translate noun phrases into Indonesian. It was used because she wants to deliver the message of the text as natural as possible

The article examines the connections between the structural and semantic correspondences of th e terms of meteorology and landscape vocabulary of the Turkish and Yakut languages in comparison with the ancient Turkic language. Lexical correspondences are divided into lexical-semantic groups: “Heavenly bodies”, “Soil, landscape”. The quantitative relationship between the Turkish and Yakut languages, as well as their representation in the ancient Turkic language, is analyzed, and a comparative analysis of the semantic stability and variability of correspondences in relation to the Turkish, Yakut and ancient Turkic languages is carried out. The semantic characteristics of lexical units are given separately according to structural types. The relevance of the article is the insufficient knowledge of the problem and connections of the Yakut language with Turkish, with which it had family ties in the early period of its development. Goal: to identify lexical parallels of the Yakut and Turkish languages, describe their structural and semantic features, determine the relationship of Yakut with Turkish. Thus, the article sets the following objectives: identifying lexical correspondences between the terms of meteorology and landscape vocabulary of the Yakut, Turkish and ancient Turkic languages; determining the stability and variability of the structural forms of Yakut lexemes in relation to Turkish and ancient Turkic forms; establishing the degree of stability and variability of the semantic-thematic meanings of the nominal stems of the terms of meteorology and landscape vocabulary of the Turkish language in relation to the Yakut forms. The methodological basis of the study was compiled by the works of domestic and foreign scientists: O. N. Böhtlingk, V. V. Radlov, S. E. Malov, A. N. Kononov, N. A. Baskakov, E. V. Sevortyan, B. A. Serebrennikov, E. R. Tenishev, G. Dörfer, E. I. Ubryatova, K. M. Musaev, A. M. Shcherbak, N. Z. Gadzhieva, V. M. Nasilov, V. M. Nadelyaev, S. Kaluzhinsky, V. I. Rassadin, I. V. Kormushin, A. V. Dybo, O. A. Mudrak, A. G. Shaikhulov, N. N. Shirobokova, G. G. Levin, and others. The main methods of work were continuous sampling method when selecting material from dictionaries and monographic works, comparative method when comparing material from different languages, descriptive-analytical method, including comparison, description and generalization, interpretation. The comparative historical method and the method of statistical analysis were partially applied.

Mandarin Chinese: A Functional Reference Grammar

Featural information (e.g., color or shape) allows interlocutors to focus their attention on the specific items under discussion from the vast set of possibilities in the environment. Intriguingly, when they are used to modify and restrict nouns, adjectives can either carry featural information themselves (e.g., green car) or retrieve featural information from the context (e.g., somebody points at a car and claims that she has the same car or a different car). Do the processing of same/different car and green car share neural correlates? For the composition of nouns with feature-carrying adjectives, prior work revealed early compositional effects (roughly 200 ms after noun onset) in the left anterior temporal lobe. However, although we know that such effects do not extend to cases of numeral quantification, which add no conceptual features to the noun (e.g., two boats), we do not know whether they extend to functional adjectives that themselves introduce no features, but instead reference features in the context. To address this question, we measured magnetoencephalography (MEG) during the processing of five types of noun phrases (NPs): same NPs (e.g., same star), different NPs (e.g., different star), color NPs (e.g., green star), comparative NPs (e.g., larger star), and another NPs (e.g., another star). Our main finding was that between 185 to 240 ms after noun onset, same and different NPs patterned with the color NPs in their elicited left temporal lobe activity, and same NPs even trended toward higher amplitudes than the color NPs. This shows that the mechanism driving combinatory effects in the left temporal cortex does not require the input words to directly name conceptual features, as long as the words reference featural information in the context, and that overlapping neural correlates underlie the composition of featural information from both linguistic and nonlinguistic sources.

The aim of this paper is to show how the various functions and forms of a noun phrase can be handled in Functional Discourse Grammar (FDG). In order to do so, I will take what may be called the standard, prototypical noun phrase as my point of departure. The standard noun phrase (i) has a nominal head; (ii) denotes a concrete, first-order entity; (iii) denotes by lexical means; and (iv) is used referentially.2 This type of noun phrase is discussed in section 3, after a brief introduction to FDG in section 2. Sections 4--7 then study noun phrases which lack one of the properties of standard noun phrases, in the order in which these properties are listed above. The conclusion will be that the separation between the interpersonal, the representational, and the morphosyntactic levels of analysis in FDG allows for a systematic treatment of standard and non-standard noun phrases.

A lot of researchers have tried to create links between Turkish and Urdu languages. Some researchers have argued that Urdu is directly influenced by Turkish Language. It might be because word “Urdu” is derived from Turkic word “Oordou” meaning “Camps” or “Armies”. Many linguists think that Urdu has less contact with Turkish as compared with Arabic and Persian Languages. On the other hand, the Grammatical Structure of Urdu Language is much more similar to the Hindi Language. No Doubt, Turkish and Urdu share a lot of words but it is Grammatical and syntactical structure that determine family of a language not vocabulary. Almost every language has loan words and with the passage of time, every language adopts new words from other languages. For example, scientific terms in almost every language are in Latin. This article is a short description of Turkish Influence on Urdu Language. I have tried to explain the similarities and differences between Turkish and Urdu Languages and cases which govern these similarities and differences.

This study aims to examine the types of noun phrases in the form of election term collocations and evaluate the readability of their translations in institutional news texts from the Constitutional Court of the Republic of Indonesia. The noun phrases that are the subject of this study are classified into three types, namely, modificative, coordinative, and appositive noun phrases, based on Widjono's theory (2005). Electoral term collocations in the form of noun phrases such as election results, election stages, and electoral districts have specific structures and meanings, so their translation requires accuracy of meaning and ease of understanding in the target language. This study uses a qualitative descriptive approach with content analysis techniques and the distribution of readability questionnaires to 25 respondents. Data were collected from 12 online news articles from the Indonesian Constitutional Court discussing disputes over the 2024 election results, with a total of 80 noun phrases. The results show that the most dominant type of noun phrase is the modificative noun phrase. Additionally, most translations of these noun phrases are considered easy to understand by the target readers, with an average readability score of 2.77 on a scale of 3. These findings indicate that the translation of noun phrases in institutional texts has achieved a good level of readability, although there are still some noun phrases that could be confusing if not adapted to internationally recognized equivalents.

In this paper, I treated inclusive overview on Istog’s linguistic areal. Observed linguistic areal, contains material of interest for inter-linguistic contemplation and comparison, because this speech, like many Albanian speeches, in the stream of time, in contact with Turkish, Serbian and other languages, has built borrowing reports with these languages. On the other hand, borrowings from Slavic and Turkish language, have offered the opportunity to benefit new lexical units, thanks to the presence of many word-forming tools, particularly suffixes. However, in the phonetic system, morphological and syntactic of the observed linguistic areal, the impact of these languages has not been powerful so much. Collecting dialectic materials from Istog with surroundings, systematization and their analyses represents a documentation that enables the reconstruction of the history of the Albanian language in these areas, so this activity is of particular importance. Article visualizations: