Semantic core identification as a method to overcome textoidness
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Jun 2026· RESEARCH RESULT Theoretical and Applied Linguistics· Vol 12· 0 citations
TL;DR
There is no reliable method to assess and achieve global semantic coherence in AI-generated translations, so this study aims to lay the foundations of a linguistic method for overcoming textoid-quality of machine translation results by means of semantic core identification.
Abstract
We believe that neural machine translation results intended to function as a text always have enough potential for a semantic core (i.e. a communicative center with text-forming properties) to be found and verbalized.
The relevanceof this article is provided by two factors. On the one hand, machine translation software is widespread, easily available, and in active use; on the other hand, machine translation results have to be post-edited to the quality of a communicative text due to systematic disruption of its intra-textual connections in the machine translation results which turns out to be, in its raw, non-edited version, a set of separate sentences, in other words – a ‘textoid’ that should be fixed by an editor to function as a coherent text. Although frequent cross-checking between the original text and its translation helps eliminate occasional semantic errors and inaccuracies, the AI output in general still looks like a poor-quality text with a ‘machine DNA.’ This brings us to the core problem: now, there is no reliable method to assess and achieve global semantic coherence in AI-generated translations.
That is why our study aims to lay the foundations of a linguistic method for overcoming textoid-quality of machine translation results by means of semantic core identification. Through a comprehensive approach that comprises such methods as abstraction, analysis, classification, synthesis, modeling, and measurement this study has achieved the following results: (a) a unique tool for semantic core identification was proposed relying on such well-known linguistic concepts as subject, predicate, and object, as well as on a basic subject-logical typology of semantic relations; (b) a need to adjust the initial core wording/formula was demonstrated in 46 % of cases; (c) the median core volume (31 %) in a textoid was determined for medical news; (d) basic principles of linguistic annotation (how to label specific linguistic, structural, or semantic features) were proposed as well as a system of notations; (e) a principle for representing the semantic core by means of graphic formulae was proposed for illustrative purposes; (f) ways for further scientific research were outlined.
Conclusion: 52 textoids were analyzed to demonstrate applicability of our method, intended to serve as a reliable linguistic tool for identifying a semantic core which, in its turn, can function as (1) a text-forming essence that can be used in converting a textoid into a text; (2) a subject-logical benchmark for controlling and verifying translation, both for specific segments of the machine translation and for the text as a whole; and (3) a tool for interpreting unclear or contradictory passages within the textoid (without direct need to check up with the source text).
It was concluded that texts generated by artificial intelligence constitute a separate linguistic phenomenon with its own set of characteristics, which requires a special typology and a flexible, updatable analysis methodology.
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Translation is defined as the process of transferring meaning from one language to another. It is an extremely difficult and complex process because it involves not only transferring words but also ideas, culture, linguistic customs, and meanings derived from syntactic elements, their arrangement, word structure, and derivation. This is especially true in languages with complex structures, such as Arabic, which is characterized by its multiple linguistic contexts. These contexts have not been adequately addressed by NLP (Natural Language Processing) applications in machine translation models due to the lack of diverse contexts where metaphor, figurative language, grammatical inflections, morphological patterns and their connotations, and sentence structure all play pivotal roles in determining meaning. Furthermore, spoken language, with its inherent phonetic and expressive characteristics, conveys the text into broader semantic spaces. These spaces are influenced by the effect of intonation on specific syllables, the speaker's psychological state, the listener's mood, and accompanying body language, which transforms meaning into other subtle details. All of this, and more, is absent from machine translation, no matter how hard its creators try to imbue it with human emotions and feelings. This study highlights the importance of integrating in-depth linguistic analysis, contextual semantic modelling, and cultural awareness into natural language processing-based translation systems. By combining traditional linguistic insights with computational methods, the research offers a framework that can contribute to improving the accuracy of machine translation from Arabic to English.
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Methods of semantic analysis and the appeal to the semantic side of the language based on Bloom’s taxonomy in the Russian language classes at a non-linguistic university in the study and analysis of the text are actual. The role of Bloom’s taxonomy in the step-by-step implementation of the content analysis algorithm and its appeal to it lies in its consistency, i.e., in the mandatory stages of conscious learning, such as knowledge, understanding, application, analysis, and synthesis. The use of this method contributes not only to the processes of memorizing and reproducing facts, but also allows you to establish a connection with the previously received information, generalize, change, interpret and transform it.In this regard, the text is an invaluable source of factual material that provides options for the diverse use of language units at various levels. In Russian language classes, the most effective methods for semantic text analysis are the associative method and the content analysis method. Tasks based on associations arouse students ‘ cognitive interest, and content analysis contributes to better assimilation of texts of different speech styles, including texts on the specialtySemantic analysis of texts based on Bloom’s taxonomy in a non-linguistic university contributes to the development of critical and creative thinking of students by forming the ability to determine the value of an idea based on a critical and objective consideration of arguments
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Languages are permanently expanding their vocabulary with new
words, which is an inevitable and constant process. The new words can be
produced by the means of one language, borrowed from a foreign language, or
created as a combination of one’s language means and borrowed elements.
These novel words appear for the first time mainly in the journalistic articles,
and then, depending on many factors, either spread into the general lexicon, or
remain a part of a specialized language. The research question relates to the
personal name-based neologisms in the Macedonian journalistic discourse
aiming to reveal the most frequent word formation devices journalists use
when creating such neologisms in their text. Thus, 2000 online news articles,
informative and opinion based, are selected by using personal name-based
neologisms as key words on Google Search. They serve as a sample, which is
purposeful and cohesive. The data analysis rests upon the general qualitative
inductive method and is enriched by the coding, i.e. organizational boxes are
established in which the data are sorted for further analysis. The research
results showcase that Macedonian journalistic discourse contains personal
name-based neologisms which support the view that they are a common
phenomenon in many modern languages. Further, the findings reveal that the
suffixation is the most frequent language means used by journalists when
generating personal name-based neologisms which reinforces previously
stated outcomes. Furthermore, from the results it is obvious that the new
personal name-based words are created with prefixes and that there are new
compounds as well. In addition, these answers unveil that the personal namebased neologisms assist in achieving stylistic expression of the journalists’ text.
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