Lecture
Lexicons, n-grams and Language Models
Description
This lecture covers the concepts of lexicons, n-grams, and language models. It starts with an introduction to lexicons and their field implementations, followed by an explanation of n-grams and probabilities. The lecture delves into the n-gram approach, smoothing techniques, and examples of language identification and spelling error correction. It emphasizes the importance of lexica for recognizing and classifying words, the effectiveness of n-grams for various tasks, and the significance of smoothing techniques in estimating n-gram probabilities.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceOfficial source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (72)
Words, tokens, n-grams and Language Models
Explores words, tokens, n-grams, and language models, focusing on probabilistic approaches for language identification and spelling error correction.
Words Tokens: Lexical Level Overview
Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.
Vision-Language-Action Models: Training and Applications
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Probability and Statistics
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Fundamental Limits of Gradient-Based Learning
Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.