Semantic Meaning Of Words Research Articles

Функция временного отклика — новый метод исследования нейрофизиологических механизмов восприятия речи в экологически валидных условиях

<p style="text-align: justify;">The temporal response function is a new method that allows to investigate the brain mechanisms of perception of natural, naturalistic speech stimuli. In contrast to other methods for studying brain activity (e.g., evoked potentials), the temporal response function does not require the presentation of a large number of uniform stimuli to produce a robust brain response - recordings of narrative speech lasting 10 minutes or more can be used in experimental paradigms, increasing their ecological validity. The temporal response function can be used to study brain mechanisms of online processing of different components of natural speech: acoustic (physical properties of the audio signal such as envelope and spectrogram), phonological (individual phonemes and their combinations), lexical (contextual characteristics of individual words) and semantic (semantic meaning of words), as well as the interaction between these components processing mechanisms. The article presents the history of the method, its advantages in comparison with other methods and limitations, mathematical basis, features of natural speech components extraction, and a brief review of the main studies using this method.</p>

Media analysis of the text on the example of Chinese strategy of information warfare

The topic of media analysis of information wars in the context of the Chinese issue is of academic interest, both for understanding current events and for improving information analysis methods and media strategies. The article assesses the influence of media on China's behavior in the information war, characterizes the challenges associated with media analysis in China's information war, describes methods and metrics for analyzing media content about the information war in China. Information technologies at the current level of development create broad opportunities for managing political conflicts both on the scale of one state and at the regional level. These technologies, which are actively used in information wars, have a number of advantages over traditional means of struggle due to their massive impact on the moral and psychological state of the population of the enemy country or individual parties, movements and political associations. The article uses a discourse analysis of the media text, identifies expressive linguistic means, the semantic meaning of words and expressions, and the communicative orientation of the text. The empirical basis of the research is the article "Celestial Truth: who and why is waging an information war with the PRC". The main conclusion of the study is that modern technologies and social media have become a powerful tool for influencing public opinion and shaping the perspective of events. The information war in China has a global impact, attracting the attention of the international community. Global media and international platforms actively monitor and cover events in the region, which can influence the perception of the conflict beyond its borders. The impact of the information war has a significant impact on public opinion. There is the use of various methods, including fake news, disinformation, to influence public sentiment and views. The Chinese concept of network warfare opens up further prospects and opportunities for the Chinese leadership and military specialists to work on the creation and implementation of the latest technology samples.

Theoretical foundations of mutual semasiological research in modern linguistics

The recent growing interest in cognitive linguistics, scientific works, monographs and textbooks written in this field are evidence of this. Language levels and trends in their development, structural approach to languages, language and speech issues, functional approach to language units, etc. the problems have begun to be seriously investigated. One such issue is the study of modern cognitive linguistics and lexical-semantic systems. Language is subject to various influences in the process of development. These effects result in changes in different layers of language, including the lexical layer. This process leads to the obsolescence of word meanings in the lexicon, the emergence of new meanings, the expansion of meaning, the narrowing of meaning, the diversification of meaning, and so on. This leads to the fact that the connection between the lexical and semantic meanings of words is also affected; new lexical-semantic word groups appear. The article is rich in quotes from the works of many scholars.

SVM Machine Learning Classifier to Automate the Extraction of SRS Elements

The process of extraction of software entities such as system, use case, and actor from an English natural language description of a user’s software requirements is a linguistic and semantic process of a natural language processing application. Entity extraction is known to be a complicated and challenging problem by researchers in the fields of linguistics or computation, due to the ambiguities in natural languages. This paper presents a named entity recognition method called SyAcUcNER (System Actor Use-Case Named Entity Recognizer), for extracting the system, actor, and use case entities from unstructured English descriptions of user requirements for the software. SyAcUcNER uses one of the Machine Learning (ML) approaches, that is, the Support Vector Machine (SVM) as an effective classifier. Also, SyAcUcNER uses a semantic role labeling process to tag the words in the text of user software requirements. SyAcUcNER is the first work that defines the structure of a requirements engineering specialized NER, the first work that uses a specialized NER model as an approach for extracting actor and use case entities from English language requirements description, and the first time an SVM has been used to specify the semantic meanings of words in a certain domain of discourse; that is the Software Requirements Specification (SRS). The performance of SyAcUcNER, which utilizes WEKA’s SVM, is evaluated using a binomial technique, and the results gained from running SyAcUcNER on text corpora from assorted sources give weighted averages of 76.2% for precision, 76% for recall, and 72.1% for the F-measure.

Learning Japanese-English Bilingual Word Embeddings by Using Language Specificity

Cross-lingual word embeddings have been gaining attention because they can capture the semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform a word embedding space from one language to another. To improve bilingual word embeddings, we propose an advanced method that adds a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of the Japanese language. We evaluated our method by comparing it with single mapping-based-models on bilingual lexicon induction between Japanese and English. We determined that our method was more effective, with significant improvements on words of Japanese origin.

Field Theory, the ‘Talking Cure,” and Metaphoric Processes

The Barangers’ field theory considers the analytic situation as dialogical, bipersonal, and at the service of the interpretation of unconscious phantasies created in the field by the shared participation of patient and analyst. They asked about the power of words to offer meaningful interpretations and their potential to return to their earlier meanings in the patient's life. Examining the richness of the spoken word as a medium for the expression of private experiences to another, I suggest that what counts in analytic work is the use of living words, linked to past and intra-analytic experiences. I suggest that in psychic life, the semantic meaning of words is embedded in their experiential and affective interpersonal meaning. The words act as the medium to link the private experience of the analysand to the private experience of the analyst without a direct intersubjective communication. I illustrate my point with the example of the analyst's warm words being savored as warm milk by a regressed analysand.

DYNAMIC PROFILE REPRESENTATION AND MATCHING IN DISTRIBUTED SCIENTIFIC NETWORKS

Finding people having similar interests in online community is an interesting but challenging problem. Especially, in distributed multidisciplinary research network, locating scientists who share common interests to collaborate and solve large scientific problems is becoming more important. This paper introduces a method for extracting and modeling scientists’ interest profiles from their day-to-day interactions and a method for semantically matching interest profiles based on latent semantic analysis. These methods exclude the necessity of having ontology for semantic matching of profiles, while still maintain the ability to reason about the semantic meaning of words.

Semi-structured document categorization with a semantic kernel

Since a decade, text categorization has become an active field of research in the machine learning community. Most of the approaches are based on the term occurrence frequency. The performance of such surface-based methods can decrease when the texts are too complex, i.e., ambiguous. One alternative is to use the semantic-based approaches to process textual documents according to their meaning. Furthermore, research in text categorization has mainly focused on “flat texts” whereas many documents are now semi-structured and especially under the XML format. In this paper, we propose a semantic kernel for semi-structured biomedical documents. The semantic meanings of words are extracted using the unified medical language system (UMLS) framework. The kernel, with a SVM classifier, has been applied to a text categorization task on a medical corpus of free text documents. The results have shown that the semantic kernel outperforms the linear kernel and the naive Bayes classifier. Moreover, this kernel was ranked in the top 10 of the best algorithms among 44 classification methods at the 2007 Computational Medicine Center (CMC) Medical NLP International Challenge.