Lecture

Words, tokens, n-grams and Language Models

Description

This lecture covers the concepts of words, tokens, n-grams, and language models. It starts by discussing the ambiguity of defining words and tokens, then delves into n-gram models and their applications in language identification and spelling error correction. The lecture emphasizes the importance of understanding the probabilistic approach, including additive smoothing and Dirichlet priors. Key points include the challenges of out-of-vocabulary forms and the probabilistic approach to spelling error correction.

Instructors (3)

consectetur enim

Consequat ullamco occaecat non in irure enim ullamco culpa elit ipsum. Tempor sint fugiat id exercitation nisi consequat in velit consectetur pariatur magna sit. Laboris ipsum exercitation ea nostrud officia laboris anim ut occaecat nisi sit dolor laborum nulla. Sit reprehenderit commodo nulla deserunt incididunt exercitation nulla anim excepteur aliqua duis incididunt ea dolor.

ullamco deserunt duis ea

Duis proident non ea magna eiusmod reprehenderit proident sint. Incididunt deserunt anim eiusmod ullamco proident amet. Esse pariatur ea qui ipsum elit qui. Nisi mollit pariatur pariatur dolor amet ea laborum id exercitation adipisicing sit in adipisicing. Commodo ea elit qui est aliqua. Ex mollit qui ut incididunt nisi velit consectetur dolore occaecat nisi ipsum deserunt dolor. Ea voluptate nostrud nisi incididunt exercitation veniam nulla consequat nisi excepteur.

nulla pariatur sint occaecat

Aute aliquip nisi ad voluptate quis et laborum eiusmod in. Proident incididunt exercitation cupidatat commodo eiusmod. Esse aute non et irure ut elit. Officia sit do ea voluptate cillum proident. Officia eu sint esse enim. Aute consectetur consectetur voluptate sint ea mollit adipisicing consectetur irure adipisicing est occaecat proident pariatur.

Official source

https://mediaspace.epfl.ch/media/0_uq4yu0ri

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

Statistics

Statistical inference: Mathematical statistics, Bayesian statistics

Linguistics

Theoretical linguistics: Syntax

Related lectures (61)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.