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Found 5 papers, 2 papers with code
Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary
However, this approach is restricted by the LM's vocabulary available for masked predictions, and its precision is subject to the context provided by the assertion.
Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling -- ORSUM 2021
This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents.
LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond
Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task.
Preference rules for label ranking: Mining patterns in multi-target relations
In CAR, the consequent is a single class, to which the example is expected to belong to.
Affordance Extraction and Inference based on Semantic Role Labeling
Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing.
