Conceptual Networks for Cross-Linguistic Idiomatic Expressions: A Feature-Based Graph Approach

SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkit

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.

From Word Embeddings to Semantic Projections: Interpretability and Context in Web-Scale Semantic Analysis

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.

Investigating Cross-Modal Semantics in Large Language Models Using Concept-Color Associations

Cross-modal correspondences, exemplified by systematic mappings between abstract concepts and colors, illuminate the mechanisms through which humans integrate sensory inputs with conceptual frameworks. Existing theories propose that these associations arise from semantic mediation via metaphors, statistical regularities in language and environment, or overlapping affective or structural features. Trained solely on textual data, large language models provide a unique perspective for examining whether such mappings can emerge from linguistic patterns alone. In this study, we collected color associations for 85 abstract concepts across temporal, alphanumeric, directional, and spatial categories from 260 Chinese university students. These human responses were then compared with outputs from 10 LLM variants across three families, GPT, Deepseek, Doubao, generating 300 responses per concept per model to capture distributional tendencies. Humans exhibited consistent conceptcolor links for 72 of the 85 concepts, with semantically proximate items, such as consecutive seasons or months revealing organized similarities in their color profiles. LLMs, in contrast, produced sharper and more peaked distributions, aligning well with humans on established conventions such as spring-green or A-red, yet diverging on others, for instance associating the concept 0 predominantly with black rather than the human preference for white. Alignment metrics were moderate overall, with the highest levels observed in the GPT family, suggesting that expansive training corpora better approximate nuanced human variability.

Do General NLP Embeddings Capture Ontological Reasoning?

General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We introduce AVA, a systematic framework for evaluating whether embeddings distinguish logic-sensitive relational semantics in ontologies and knowledge graphs. AVA comprises 171,007 contrastive triplets derived from 163 heterogeneous ontologies using hierarchy inversion, relation substitution, and disjointness injection. Each triplet contains an ontology statement, a semantically equivalent paraphrase, and a logic-sensitive hard negative with contradictory relational meaning. We evaluate more than 25 state-of-the-art embedding models and find substantial limitations: the best model achieves only 0.739 triplet accuracy, while hard negative accuracy falls to 0.135. Fine-tuning improves discrimination by a large margin but transfers poorly to downstream Semantic Web tasks, including taxonomy discovery and ontology alignment. Further analysis suggests that improvements stem partly from perturbation-specific pattern recognition rather than robust ontological understanding. These findings reveal a persistent gap between linguistic representation learning and ontology-level discrimination, challenging the assumption that strong NLP benchmark performance translates to Semantic Web competence.

Semantic analysis of problems in natural language processing and their mathematical interpretation

Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.

Aligning Embedding Spaces Across Languages to Identify Word Level Equivalents in the Context of Concreteness and Emotion