This paper proposes a training-free inference-stage enhancement termed Semantic-Sharpened Masked Language Modeling, which achieves performance comparable to knowledge-enhanced models without additional training or structural modifications, thereby improving the balance between overall accuracy and sense-level discrimination.
A robust WSD model that integrates a bidirectional long shortterm memory network (BiLSTM) with an attention mechanism, specifically designed for Chinese patent texts is proposed, providing reliable support for knowledge mining and intelligent text processing in technical domains.
L.-X. Gao, T. Dong, M.-H. Yang· Advanced Electromagnetics· 0 citations
Abstract Identifying lexical semantic differences across closely related language varieties remains methodologically challenging, as traditional approaches often rely on intuition, manual inspection, or researcher-constructed word lists with limited coverage. This study proposes a computational framework that uses word...
Hongzhi Xu, Yuan-Bin Zhao, Xueyi Wen et al.· Corpus Linguistics and Lingu...· 0 citations
A layer-wise analysis indicates that surface-level features such as temporality and negation are captured more reliably than deeper semantic phenomena like quantification in large language models, highlighting the limited capacity of current LLMs to generate fully formal meaning representations.
Rémi De Vergnette, Maxime Amblard· International Conference on...· 0 citations
It is found that providing candidate senses, similar to what is done in traditional WSD, improves WiC performance in all settings and helps models make more consistent and targeted judgements.
Yi Zhou, Kiamehr Rezaee, Danushka Bollegala et al.· 0 citations
SLITE is presented, an explainable hybrid model for Recognizing Textual Entailment that integrates two complementary layers of semantic analysis: a structural-relational layer, based on semantic compatibility and incompatibility between compositional entities, and a distributional-informational layer, based on structur...
David Torres-Moreno, J. Hermosillo-Valadez, Asela Reig-Alamillo· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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