Transfer Learning for Human & AI

Distributional Semantic Model (DSM) evaluation lack a thorough comparison with respect to tested models, semantic tasks,and benchmark datasets . We borrow from cognitive neuroscience the methodology ofRepresentational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models . RSA reveals important differences related to the frequency and part-of-speech of lexical items . The results show that theleged superiority of predict based models is more apparent than real, and surely not ubiquitous . static DSMs surpass contextualizedrepresentations in most out-of context semantic tasks and datasets . Static DSMs surpassed contextualized representations in most . of most out of context semantic . tasks and . most out .of-context semantic tasks . datasets and datasets, according to the authors of this paper . The authors say. The results are published on December 1, 2013, at Springer Springer Publishing Publishing Publishing House, Springer Publishing Company, Ltd., published by Springer Publishing Group, Ltd. is published on October 1, 2009,

Author(s) : Alessandro Lenci, Magnus Sahlgren, Patrick Jeuniaux, Amaru Cuba Gyllensten, Martina Miliani

Links : PDF - Abstract

Code :


Keywords : semantic - publishing - tasks - springer - datasets -

• Data Scientist with Python
• Kaggle Learning
• Introduction to TensorFlow for AI, DL and ML by Andrew Ng
• AI Programming with Python
• Learning How to Learn on Coursera
• Bioinformatics Specialization on Coursera
• 3Blue1Brown
• Complete Python Bootcamp