TL;DR
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.
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 remaini...
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.
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...
This work presents adaptive pseudo-labeling for topic modeling (APT), a self-supervised learning framework designed to alleviate the need for labeled data, and integrates proxy-based deep metric learning into topic modeling to improve semantic coherence and diversity across topics.
This work establishes the first transformer-based benchmark for role identification in human activity descriptions and provides a scalable foundation for advanced activity reasoning and knowledge extraction.
An Ambiguity-Aware Semantic Fusion Framework (AAK-LASFNet) for robust text classification and demonstrates that the proposed framework consistently outperforms strong baselines in terms of Accuracy, highlighting its effectiveness in alleviating semantic ambiguity in text classification.
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