Semantic Term-Term Coupling based Feature Enhancement of User Profiles in Recommendation Systems

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.

SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine

Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.

Semantic relations in text and translation Die semantischen Relationen im Text und Übersetzung

Based on the German language department’s theoretical and practical aspects as well as educational programs, the present study discusses the semantic relations in text sentences and their role in the science of translation. Through clarifying the semantic relationship between the text sentence and the methods used to express a news item, a situation or an occurrence and through the statement of the multiple theoretical semantic structures of the text’s construction and interrelation, a translator can easily translate a text into the target language. It is known that language learners face multiple difficulties in writing and creating an integrated, coherent and intelligible text, and the reason for this is their lack of knowledge of semantic relations. Deutsch In dieser Forschungsarbeit gehen die folgenden theoretischen Ausführungen im Zusammenhang mit bestimmten didaktischen Bemerkungen, die im Laufe des Lehrprozesses aufgetreten sind, der Frage der semantischen Relationen in Satzperspektiven nach. Berücksichtigt wurde dabei überdies das Lehrprogramm in der irakischen Germanistikabteilung. Daher besteht das Hauptpostulat dieser wissenschaftlichen Abhandlung in dem Versuch, dieses sprachliche Phänomen im Übersetzen und dessen vielseitige Varianten darzulegen sowie dessen Anwendbarkeit in den Verhandlungen, im praktischen Leben und im Umgang mit den ausländischen Firmen etc. zu klären.

Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics

Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data are one of the most commonly used modes of data in diverse applications such as healthcare and finance. Much of the existing interpretability methods used for tabular data only report feature-importance scores—either locally (per example) or globally (per model)—but they do not provide interpretation or visualization of how the features interact. We address this limitation by introducing Feature Vectors, a new global interpretability method designed for tabular datasets. In addition to providing feature-importance, Feature Vectors discovers the inherent semantic relationship among features via an intuitive feature visualization technique. Our systematic experiments demonstrate the empirical utility of this new method by applying it to several real-world datasets. We further provide an easy-to-use Python package for Feature Vectors.

Proximal versus distant suffering in TV news discourses on COVID-19 pandemic

News is central to human communication and has an important signifying power as a particular subsystem within language. This study sets out to comprehensively examine how four major TV global news providers – CNN, BBC, DW and RT – have covered the COVID-19 pandemic from outbreak to mid-crisis. We apply a multi-level content analysis approach that rests on theories of proximization and representation of distant suffering, following a computer-assisted analysis that aids in identifying concepts occurrence and the semantic relationship among the highly frequent clusters. We explore the news representation during 2020 of COVID-19 as proximal versus distant discourses of suffering, safety and compassion conceptualized in light of theories on distant suffering. A total number of 12 dataset reports consisting of 2,017,875 words were analyzed. The results suggest that the COVID-19 pandemic news formulates a particular type of discourse on suffering that individualizes the sufferer, sets out the course of action and turns the fast-approaching pandemic into a global cause for action.

Diversification as codified digital administrative and legal management system: interinfrastructural provision of the information capital

The article examines diversification as a system of administrative and legal management in the intersectoral provision of digitalization in Ukraine, which expands the semantic relationship between law and economics in the modern quantum-electronic world. Diversification is shown as a legal policy of the world order, which is due to a planned and creative relationship to protect the legal needs and interests of the individual, the state and society. This paper analyzes the prospects of diversification as a digital codification system of administrative and legal management in the inter-infrastructure of information capital. This article is devoted to highlighting the diversification mechanism for the implementation of current legislation in the field of critical infrastructure protection.

Emotive verbs of the Russian language in the semantic and grammatical aspect

The article is devoted to the consideration of the lexical-grammatical and semantic features of converting pairs of emotive verbs in the Russian language such as “????????? – ???????, ??????????? – ?????????, ????????? – ???????, ???????? – ??????”. These verbs form an opposition having a grammatical, derivational and semantic character. In terms of the semantic relationship, the transitive verb is more complex, since it contains “causation”, while the formal relationship, on the contrary, means greater complexity of the reflexive verb, which has a postfix “-??”. The constitutive semantic features of emotive verbs of the Russian language are “unintentional action”, “focus on the object” (for a transitive verb) and self-isolation (reflexive verb), the ability to describe an emotional state, emotional experience, emotional attitude.

Intensification for discursive evaluation: a corpus-pragmatic view

The phenomenon of intensification is pervasive in natural language use. Previous research has extensively discussed what intensifiers are and how they are associated with semantic developments. Corpora prove to be a useful tool to examine the semantic dimension of intensifiers. What has been overlooked, however, is the internal structure(s) of meaning conveyed by “intensifier + adjective” constructions in naturally occurring text and speech. The semantic relationship between the intensifier and the modified adjective needs to be made more explicit to address the pragmatics of intensification. Using BNC Sampler (a part-of-speech tagged corpus of general English) this study examines the most frequently used adjective intensifiers in both written and spoken discourse. Concordance lines generated for the adjective intensifiers are used to illustrate evaluative expressions in context. The observation contributes to debates on the pragmatics of intensifiers for evaluative meaning construction and transmission.

Development of a Staff Informant Measure of Lucidity

Lucidity Measure Development: An existing questionnaire measuring lucidity length, degree, content, coinciding circumstances, and time from lucid episode to death was expanded to include time of day, expressive and receptive communication and speech the month prior to and during the lucid event. Pilot Study: 33 interviews with staff were conducted; 73% reported ever witnessing paradoxical lucidity. Among 29 events reported, 31% lasted several days, 20.7%, 1 day, and 24.1% less. In 78.6% the patient engaged in unexpected activity. 20% died within 3 days and 17% within 3 months after the event. Qualitative Analyses: To refine the measure, 10 family caregivers and 20 LTSS staff caregivers completed a web-based focus-group type exercise using QualtricsXM. A content-thematic analysis with an inductive approach was applied to make qualitative inferences by analyzing the meaning and semantic relationship of words, phrases, and concepts. Using the reduction method of selection, conceptual content categories will be developed.

Exploiting multisensory encoding of names via name tags facilitates remembering

Associating names to faces can be challenging, but it is an important task that we engage in throughout our lives. An interesting feature of this task is the lack of an inherent, semantic relationship between a face and name. Previous scientific research, as well as common lay theories, offer strategies that can aid in this task (e.g., mnemonics, semantic associations). However, these strategies are either impractical (e.g., spaced repetition) or cumbersome (e.g., mnemonics). The current study seeks to understand whether bolstering names with cross-modal cues—specifically, name tags—may aid memory for face and name pairings. In a series of five experiments, we investigated whether the presentation of congruent auditory (vocal) and written names at encoding might benefit subsequent cued recall and recognition memory tasks. The first experiment consisted of short video clips of individuals verbally introducing themselves (auditory cue), presented with or without a name tag (visual cue). The results showed that participants, cued with a picture of a face, were more likely to recall the associated name when those names were encoded with a name tag (i.e. a congruent visual cue) compared to when no supporting cross-modal cue was available. Subsequent experiments probed the underlying mechanism for this facilitation of memory. The findings were consistent with a benefit of multisensory encoding, above and beyond any effect from the availability of multiple independent unisensory traces. Overall, these results extend previous findings of a benefit of multisensory encoding in learning and memory, to a naturalistic associative memory task.