Opposition Relations among Verb Frames

The article reveals the features of linguistic research tasks as a means of achieving different types of educational results: subject, meta-subject and personal. The characteristics of research tasks constructed as a system of tasks organizing the main stages of children’s linguistic research are substantiated: problem situation; awareness of a cognitive issue; building hypotheses; application of methods used in linguistic science (observation, classification, language experiment); presentation of a report (verbal or graphic). The possibility of using this model for the development of research linguistic tasks in extracurricular work with primary schoolchildren is shown. Examples of such tasks are given.

Voices from the Lost Horizon: Stories and Songs of the Great AdamaneseAnvita Abbi (2021)New Delhi: Niyogi Books. Pp. 176ISBN: 978-93-91125-06-6 (pbk)

Different lexical resources may pursue different views on lexical meaning. However, all of them deal with lexical items as common basic components, which are described according to criteria that may vary from one resource to another. In this paper, we present a method for measuring the degree of similarity between a valency-based lexical resource and a WordNet. This is motivated by both theoretical and practical reasons. As for the former, we wonder if there are lexical classes that "impose" themselves regardless of the fact that they are explicitly recorded as such in source lexical resources. As for the latter, our work wants to contribute to the research task dealing with merging lexical resources. In order to apply and evaluate our method, we propose a normalized coefficient of overlapping that measures the overlapping rate between a valency lexicon and a WordNet. In particular, in the context of the exploitation of the linguistic resources for ancient languages built over the last decade, we compute and evaluate the overlapping between a selection of homogeneous lexical subsets extracted from two lexical resources for Latin.

This chapter presents the results of a fine-grained grammatical analysis of live commentaries of football (soccer) games, and so adds to a growing body of literature concerned with the linguistics of sport and, therein, particularly with football. More specifically, two subtly different types of football commentary are compared via a linguistic analysis: U.K.-produced live radio and television commentaries. The linguistic inquiry here explores how the experiences reported in these commentaries are encoded grammatically in the language used. As such, a detailed and large-scale analysis of the commentary data is conducted using Systemic Functional Linguistics, specifically a Transitivity analysis. In Systemic Functional theory where language is seen as inherently related to social context and culture, such an analysis can tell us a lot about the data contextually. The paper starts by introducing the Transitivity system within Halliday’s Systemic Functional Linguistics. This is followed by an introduction to the data analysed, explaining the contextual attributes and offering some preliminary linguistic characteristics of the component parts of the dataset. The body of the chapter presents the results of the analysis of Transitivity undertaken on the commentary data, divided into three parts to reflect three pertinent trends consequent from the data analysis.

This paper aims to present an analysis of the argument structure of three Italian verbs that are associated with the ‘commercial transaction’ frame (comprare “to buy”, vendere “to sell”, pagare “to pay”), in the light of a constructionist and usage-based approach. The discussion runs as follows: The first part is a theoretical review of the studies on argument structure, Construction Grammar, and usage-based models. This framework is then applied to a corpus of data, in order to extract the syntactic and semantic frames of these verbs, both in their most prototypical constructions and in other, less usual, patterns. Finally, it is shown how this methodology can capture the wide range of variation that characterizes a number of lexically-complex constructions that are distributed along the lexicon-syntax-semantics continuum.

Lexical-Syntactic Information in Aphasia: Verb Complementation Frames in Production and Repetition Tasks

The use of lexical resources in linguistic analysis has expanded rapidly in recent years. However, most lexical resources, such as WordNet or online dictionaries, at this point do not usually indicate figurative meanings, such as conceptual metaphors, as part of a lexical entry. Studies that attempt to establish the relationships between literal and figurative language by detecting the connectivity between WordNet relations usually do not deal with linguistic data directly. However, the present study demonstrates that SUMO definitions can be used to identify the source domains used in conceptual metaphors. This is achieved by identifying the relationships between metaphorical expressions and their corresponding ontological nodes. Such links are important because they show which lexical items are mapped under which concepts. This, in turn, helps specify which lexical items in electronic resources involve conceptual mappings. Looking specifically at the concept of PERSON, this work also establishes connectivity between lexical items which are related to “Organism.” Therefore, the methodology reported herein not only aids the categorizing of lexical items according to their conceptual domains but also can establish links between these items. Such bottom-up and top-down analyses of lexical items may provide a means of representing metaphorical entries in lexical resources.

The corpus constituents are actually the miniature of language. As the computer technology continuously progresses, corpus-based research proves increasingly applicable to language teaching and learning. As a free corpus analysis software program designed specifically for use in the language classrooms, AntConc provides the users with a powerful toolkit with various features to analyze the loaded written texts. Basic functions like the concordance tool and cluster sections of Antconc demonstrate the possibility to assess the performance of students' language proficiency. Considering the fact that IELTS is widely recognized as a standardized test and a big number of Chinese students are preparing for it in order to study abrord, the present study managed to built a self-constructed corpus of students essays in the English writing course. By means of the sophisticated coding and tagging systems, we obtained a set of standard text materials attached with the label of part of speech. Based upon the data analysis of the students’ IELTS writing samples against the modal essay corpus, we conducted the corresponding evaluation of students' lexical resource and offer further suggestions and tailor-made solutions in order to help students improve their writing ability. Under the help of the model essay corpus, students might obtain useful instructions, increase grammar diversity and accuracy, thereby become more competent writers. Our study added more evidence to the conclusion that corpus has a great potential to fundamentally change the ways we approach language education. Apparently, teachers working with highly proficient students from diverse backgrounds need to be informed about the latest corpus tools for a more fine-tuned or specifically tailored application in their writing course.

The current research aimed to uncover the use of the descriptive qualitative method in Korean linguistic studies, with the main objective of investigating how this method is applied in such research. This study is not merely theoretical but also practical, as it provides guidelines for conducting research in Korean linguistics using descriptive qualitative approaches. The objects of this study were scholarly articles and relevant books, as well as previous research reports selected through purposive sampling, analyzed using a qualitative approach with a library research method and documentation techniques for data collection. The findings reveal six stages in conducting Korean linguistic research with descriptive qualitative methods: problem identification, literature review, determining research objectives, data collection, data analysis and interpretation, and reporting of findings. Furthermore, data analysis involves four essential steps, namely data collection, data reduction, data display, and drawing conclusions. Although the descriptive qualitative method proves beneficial for research in Korean linguistics, it still presents challenges, particularly the potential subjectivity of researchers during data interpretation. Therefore, this research suggests that future studies should be supported by alternative perspectives, such as quantitative methods or a mixed-methods approach that combines both quantitative and qualitative methodologies in linguistic research.

This paper explores the potential of generative artificial intelligence technology, specifically ChatGPT, for advancing corpus approaches to discourse studies. The contribution of artificial intelligence technologies to linguistics research has been transformational, both in the contexts of corpus linguistics and discourse analysis. However, shortcomings in the efficacy of such technologies for conducting automated qualitative analysis have limited their utility for corpus approaches to discourse studies. Acknowledging that new technologies in data analysis can replace and supplement existing approaches, and in view of the potential affordances of ChatGPT for automated qualitative analysis, this paper presents three replication case studies designed to investigate the applicability of ChatGPT for supporting automated qualitative analysis within studies using corpus approaches to discourse analysis.The findings indicate that, generally, ChatGPT performs reasonably well when semantically categorising keywords; however, as the categorisation is based on decontextualised keywords, the categories can appear quite generic, limiting the value of such an approach for analysing corpora representing specialised genres and/or contexts. For concordance analysis, ChatGPT performs poorly, as the results include false inferences about the concordance lines and, at times, modifications of the input data. Finally, for function-to-form analysis, ChatGPT also performs poorly, as it fails to identify and analyse direct and indirect questions. Overall, the results raise questions about the affordances of ChatGPT for supporting automated qualitative analysis within corpus approaches to discourse studies, signalling issues of repeatability and replicability, ethical challenges surrounding data integrity, and the challenges associated with using non-deterministic technology for empirical linguistic research.

A comprehensive guide to conducting research projects in linguistics, this book provides a complete training in state-of-the-art data collection, processing, and analysis techniques. The book follows the structure of a research project, guiding the reader through the steps involved in collecting and processing data, and providing a solid foundation for linguistic analysis. All major research methods are covered, each by a leading expert. Rather than focusing on narrow specializations, the text fosters interdisciplinarity, with many chapters focusing on shared methods such as sampling, experimental design, transcription and constructing an argument. Highly practical, the book offers helpful tips on how and where to get started, depending on the nature of the research question. The only book that covers the full range of methods used across the field, this student-friendly text is also a helpful reference source for the more experienced researcher and current practitioner.

Computational linguistic and natural language processing automation tasks require text annotated with tags that represent the desired output of the task. The annotation tags serve for training, validation, and evaluation. Arabic morphological analysis, and tags associated with it such as part of speech and gloss tags, is key to Arabic computational linguistics and natural language processing. Several manual and automated tagging tools exist for text. Very few exist that are based on Arabic morphological analysis. In this paper, we present an open source tagging tool with visual interface that enables the construction of annotated Arabic text corpora with automatic morphology-based tags. The tool allows the specification of tags with Boolean formulae where the atomic predicates are match and contain relations between the morphological solution of part of the text and the value of a morphological feature. The tool allows the user to directly enter manual tags, to edit existing tags through a tag sensitive coloring interface, to compare tag sets, and compute accuracy results.

More and more researchers in linguistics use large-scale experiments to test hypotheses about the data they research, in addition to more traditional informant work. In this paper we describe a new set of free, open-source tools that allow linguists to post studies online, turktools. These tools allow for the creation of a wide range of linguistic tasks, including grammaticality surveys, sentence completion tasks, and picture-matching tasks, allowing for easily implemented large-scale linguistic studies. Our tools further help streamline the design of such experiments and assist in the extraction and analysis of the resulting data. Surveys created using the tools described in this paper can be posted on Amazon’s Mechanical Turk service, a popular crowdsourcing platform that mediates between ‘Requesters’ who can post surveys online and ‘Workers’ who complete them. This allows many linguistic surveys to be completed within hours or days and at relatively low costs. Alternatively, researchers can host these randomized experiments on their own servers using a supplied server-side component.

One of the central problems considered by the Moscow School of Psycholinguistics is the analysis of ethno-cultural language consciousness specificity of speakers belonging to different language and culture communities. This article deals with psycholinguistic comparative analysis of categorization of the concept ОСЕНЬ (Autumn / Fall) in Russian and Chinese language consciousness. The aim of this article is to identify cultural and national features of categorization of the concept ОСЕНЬ (Autumn / Fall) in Russian and Chinese language consciousness. The article considers the data from different Russian and Chinese dictionaries (Russian associative dictionary, mythological, etymological, explanatory and semantic dictionaries of the Russian and Chinese languages) as well as the results of the associative experiment involving the stimulus ОСЕНЬ (Autumn / Fall), conducted for Chinese speakers. The article constructs associative fields of the lexeme ОСЕНЬ (Autumn / Fall) and highlights the basic cognitive categories of the respective concept in Russian and Chinese language consciousness. The article uses methods of statistical, quantitative, component, semantic, psycholinguistic and conceptual analyses, as well as analysis of associative experiment data. Since comparative analysis of the concept ОСЕНЬ (Autumn / Fall) in Russian and Chinese language consciousness has not been carried out in linguistic research yet, the results obtained are relevant for the development of associative lexicography, psycholinguistics, ethnopsycholinguistics and cultural linguistics. The practical significance of the work lies in the possibility of creating a bilingual associative dictionary (on the material of the Russian and Chinese languages) and the use of the data obtained in the process of teaching psycholinguistics and intercultural communication.

The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates. However, this approach relies heavily on syntactic information predicted by parsers, and suffers from errorpropagation. To remedy this problem, we introduce a model that uses grid-type recurrent neural networks. The proposed model automatically induces features sensitive to multi-predicate interactions from the word sequence information of a sentence. Experiments on the NAIST Text Corpus demonstrate that without syntactic information, our model outperforms previous syntax-dependent models.