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Found 10 papers, 3 papers with code
Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning
Commonsense is a quintessential human capacity that has been a core challenge to Artificial Intelligence since its inception.
Assessing Wordnets with WordNet Embeddings
An effective conversion method was proposed in the literature to obtain a lexical semantic space from a lexical semantic graph, thus permitting to obtain WordNet embeddings from WordNets.
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification
Experimental results show that transfer learning techniques are beneficial to the task and that current methods may be insufficient to leverage commonsense knowledge from different lexical semantic families.
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness
Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a vector space, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately?
ELRI: A Decentralised Network of National Relay Stations to Collect, Prepare and Share Language Resources
We describe the European Language Resource Infrastructure (ELRI), a decentralised network to help collect, prepare and share language resources.
The MWN.PT WordNet for Portuguese: Projection, Validation, Cross-lingual Alignment and Distribution
The objective of the present paper is twofold, to present the MWN. PT WordNet and to report on its construction and on the lessons learned with it.
Reproduction and Revival of the Argument Reasoning Comprehension Task
Given a recent publication that pointed out spurious statistical cues in the data set used in the shared task, and that produced a revised version of it, we also evaluated the reproduced systems with this new data set.
Whom to Learn From? Graph- vs. Text-based Word Embeddings
Vectorial representations of meaning can be supported by empirical data from diverse sources and obtained with diverse embedding approaches.
Predicting Brain Activation with WordNet Embeddings
The task of taking a semantic representation of a noun and predicting the brain activity triggered by it in terms of fMRI spatial patterns was pioneered by Mitchell et al. 2008.
