This paper revisits semantic projections and related count-based representations as interpretable directional semantic structures for semantic analysis in document corpora and web-based information environments and demonstrates that semantic projections effectively capture persistent contextual structures while remaining sensitive to corpus-specific discourse communities.
Abstract
Recent developments in NLP and web-scale document analysis have increasingly emphasized the importance of interpretability and contextual dependence in semantic representations. Although modern word embeddings achieve remarkable empirical performance, their semantic structure is often difficult to interpret, since meaning is encoded through latent geometric relations in high-dimensional spaces. This paper discusses an alternative conceptual framework based on explicit contextual semantic relations. Building on ideas from distributional semantics, co-occurrence analysis, and fuzzy set theory, the study revisits semantic projections and related count-based representations as interpretable directional semantic structures for semantic analysis in document corpora and web-based information environments. In this setting, several classical association measures, including PMI and related transformations, may be understood as derived from simpler conditional semantic projections. The methodology is illustrated through a comparative analysis of semantic associations related to “ChatGPT” across general web-scale data and specialized scientific repositories. Our results demonstrate that semantic projections effectively capture persistent contextual structures while remaining sensitive to corpus-specific discourse communities. The resulting perspective emphasizes interpretability, asymmetry, contextual dependence, and direct empirical meaning as central principles for semantic representation.
Compared to end-to-end classification models, this method of constructing an embedding space based on cross-level semantic metric learning enhances the ability to learn textual causality features and significantly reduces the dependency on annotated data.
Jiajun Li, Bo Shen, Guangzhi Lang· Knowledge and Information Sy...· 0 citations
It is proved that logical embeddings encapsulate the logical semantics of an argument, allowing for a better representation of its meaning, and that this encoding is optimal, in the sense that no logical information is lost in the process.
Word Sense Disambiguation (WSD) is a critical task in Natural Language Processing (NLP) that aims to determine the intended meaning of ambiguous words based on their contextual usage. Although Transformer-based language models have significantly improved contextual understanding, their decision-making process often lacks semantic transparency and explainability. Conversely, knowledge-based approaches leverage lexical databases and ontological resources to provide interpretable semantic relationships but are constrained by limited adaptability to diverse linguistic contexts.
This research introduces a hybrid WSD framework that combines contextual representations generated by Transformer architectures with structured semantic knowledge extracted from ontology-driven repositories. By integrating contextual embeddings with knowledge embeddings, the proposed model enhances both contextual sensitivity and semantic consistency during the sense prediction process. The knowledge infusion mechanism enables the model to validate contextual interpretations using explicit semantic relationships, thereby improving the reliability of word sense assignments.
Experimental evaluation demonstrates that the proposed framework achieves superior performance compared with standalone contextual and knowledge-based approaches, yielding statistically significant improvements in precision, recall, and F1-score. The results indicate that combining deep contextual learning with structured semantic knowledge provides a robust and interpretable solution for accurate word sense disambiguation across diverse linguistic scenarios
Roopa H. R., P. S., Meenatchi Sundaram· THE SCIENTIFIC TEMPER· 0 citations
The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as a mapping problem preserving distributional properties, an optimization problem minimizing loss through gradient-based methods, and an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.
D. Akhmedjanova· Международный Журнал Теорети...· 0 citations
Lexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing. Diachronic corpus research instead examines interpretable dimensions such as syntactic behaviour, morphology, and constructional patterns, but typically through separate analytical workflows. We present SynFlow, an open-source toolkit for multidimensional diachronic analysis of linguistic usage. SynFlow converts linguistic observations into period-specific distributions and applies a shared workflow across dependency-based co-occurrences, morphological features, constructional configurations, and externally derived representations such as Frame Semantics. It supports different distance measures, together with value-level decomposition, statistical testing, and incremental clustering of lexical fillers. We demonstrate SynFlow through a qualitative case study of the German adjective viral, showing how a single semantic development is reflected across syntactic, lexical, constructional, and morphological dimensions. We further report previously published results on SemEval-2020 Task 1 to situate the performance of these representations relative to existing lexical semantic change detection systems.
Bach Phan-Tat, K. Heylen, Dirk Geeraerts et al.· 0 citations
AVA is introduced, a systematic framework for evaluating whether embeddings distinguish logic-sensitive relational semantics in ontologies and knowledge graphs, and reveals a persistent gap between linguistic representation learning and ontology-level discrimination, challenging the assumption that strong NLP benchmark performance translates to Semantic Web competence.
Hamed Babaei Giglou, Jennifer D’Souza, S. Auer· 0 citations
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