Lecture

Information retrieval: vector space

In course
DEMO: sunt est
Do quis excepteur mollit cupidatat velit deserunt ad enim. Exercitation pariatur ut elit id reprehenderit duis deserunt occaecat. Adipisicing consectetur magna anim esse cillum adipisicing aute nulla irure occaecat laboris commodo officia. Ex voluptate culpa veniam eu anim ex ea pariatur deserunt ut. Labore veniam cupidatat sunt ex officia dolor sit in Lorem minim.
Login to see this section
Description

This lecture covers the basics of information retrieval, focusing on vector space models. Topics include similarity computation, cosine similarity, term frequencies, inverted files, and Fagin's algorithm. Practical exercises involve implementing relevance feedback and parallel scanning of posting lists.

Instructor
id esse
Est aliquip do excepteur mollit sit mollit Lorem. Sit commodo excepteur aliquip aute veniam tempor tempor sit. Sit aute ex officia adipisicing. Non excepteur cupidatat reprehenderit adipisicing id Lorem qui id aliquip eu laborum id. In do voluptate laborum commodo. Non sint consequat culpa anim dolor fugiat duis velit sunt tempor deserunt voluptate nisi. Cupidatat ullamco deserunt do occaecat reprehenderit fugiat cillum eu esse incididunt.
Login to see this section
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.
Related lectures (40)
Latent semantic indexing: inverted files
Explores term-offset indices in inverted files and relevance feedback solutions.
Data Wrangling with Hadoop
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
Vector Space Semantics (and Information Retrieval)
Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.
Information Retrieval Basics
Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Spark Data Frames
Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.
Show more

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.