Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Data-Mining
Text Retrieval and Search Engines
von
Über diesen Kurs
23,654 kürzliche Aufrufe
Karriereergebnisse der Lernenden
50%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
67%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
29%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Kompetenzen, die Sie erwerben
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Karriereergebnisse der Lernenden
50%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
67%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
29%
erhalten Sie eine Gehaltserhöhung oder Beförderung
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 6 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 18 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
Lehrplan - Was Sie in diesem Kurs lernen werden
2 Stunden zum Abschließen
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 Stunden zum Abschließen
2 Videos (Gesamt 15 min), 6 Lektüren, 2 Quiz
6 Lektüren
Welcome to Text Retrieval and Search Engines!10m
Syllabus10m
About the Discussion Forums10m
Updating your Profile10m
Social Media10m
Course Errata10m
2 praktische Übungen
Orientation Quiz15m
Pre-Quiz30m
4 Stunden zum Abschließen
Week 1
During this week's lessons, you will learn of natural language processing techniques, which are the foundation for all kinds of text-processing applications, the concept of a retrieval model, and the basic idea of the vector space model.
4 Stunden zum Abschließen
6 Videos (Gesamt 94 min), 1 Lektüre, 2 Quiz
6 Videos
Lesson 1.2: Text Access9m
Lesson 1.3: Text Retrieval Problem26m
Lesson 1.4: Overview of Text Retrieval Methods10m
Lesson 1.5: Vector Space Model - Basic Idea9m
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation17m
1 Lektüre
Week 1 Overview10m
2 praktische Übungen
Week 1 Practice Quiz1h
Week 1 Quiz1h
4 Stunden zum Abschließen
Week 2
In this week's lessons, you will learn how the vector space model works in detail, the major heuristics used in designing a retrieval function for ranking documents with respect to a query, and how to implement an information retrieval system (i.e., a search engine), including how to build an inverted index and how to score documents quickly for a query.
4 Stunden zum Abschließen
6 Videos (Gesamt 102 min), 1 Lektüre, 2 Quiz
6 Videos
Lesson 2.2: TF Transformation9m
Lesson 2.3: Doc Length Normalization18m
Lesson 2.4: Implementation of TR Systems21m
Lesson 2.5: System Implementation - Inverted Index Construction18m
Lesson 2.6: System Implementation - Fast Search17m
1 Lektüre
Week 2 Overview10m
2 praktische Übungen
Week 2 Practice Quiz1h
Week 2 Quiz1h
7 Stunden zum Abschließen
Week 3
In this week's lessons, you will learn how to evaluate an information retrieval system (a search engine), including the basic measures for evaluating a set of retrieved results and the major measures for evaluating a ranked list, including the average precision (AP) and the normalized discounted cumulative gain (nDCG), and practical issues in evaluation, including statistical significance testing and pooling.
7 Stunden zum Abschließen
6 Videos (Gesamt 75 min), 2 Lektüren, 3 Quiz
6 Videos
Lesson 3.2: Evaluation of TR Systems - Basic Measures12m
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115m
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210m
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10m
Lesson 3.6: Evaluation of TR Systems - Practical Issues15m
2 Lektüren
Week 3 Overview10m
Programming Assignments Overview10m
2 praktische Übungen
Week 3 Practice Quiz1h
Week 3 Quiz1h
4 Stunden zum Abschließen
Week 4
In this week's lessons, you will learn probabilistic retrieval models and statistical language models, particularly the detail of the query likelihood retrieval function with two specific smoothing methods, and how the query likelihood retrieval function is connected with the retrieval heuristics used in the vector space model.
4 Stunden zum Abschließen
7 Videos (Gesamt 88 min), 1 Lektüre, 2 Quiz
7 Videos
Lesson 4.2: Statistical Language Model17m
Lesson 4.3: Query Likelihood Retrieval Function12m
Lesson 4.4: Statistical Language Model - Part 112m
Lesson 4.5: Statistical Language Model - Part 29m
Lesson 4.6: Smoothing Methods - Part 19m
Lesson 4.7: Smoothing Methods - Part 213m
1 Lektüre
Week 4 Overview10m
2 praktische Übungen
Week 4 Practice Quiz1h
Week 4 Quiz1h
Bewertungen
Top-Bewertungen von TEXT RETRIEVAL AND SEARCH ENGINES
von JHSep 20, 2016
Great course for those trying to understand how ro analyse and process text data. It has the right amount of tools to help you understand the basics of information retrieval and search engines.
von SXMar 27, 2018
I have learned a lot of concepts through this course, but at a shallow level. It is a great introduction course to IR. It can be improved by adding more programming tasks for hands-on exercise
von WPMay 27, 2017
I found that there were a lot of mathematical function in this course. I need to see the examples of how are those functions applied in the real programming which support the business.
von ASFeb 17, 2020
This is course provide me a detailed overview of the search engine and some new concepts that I am not aware of . I think now I understand text retrieval techniques
von LLNov 20, 2018
Some mistakes in subtitle. and it is better to break down the long videos into smaller sections, illustrate the concepts with more graphs,picture and animation.
von ARSep 1, 2016
The explanation is detailed and clear. But the missing star is because I hoped the course would have programming assignments using popular language as python.
von PMAug 28, 2016
A great overview of text retrieval methods. Good coverage of search engines. A longer course will cover search engine better (remember this is a 6 weeker)
von GIAug 20, 2016
Excellent ! Well organized, presented with aptitude to detail. Definitely will recommend and take further units in this specialization.\n\nThanks Prof
von PESep 16, 2019
This course goes through the basics of text retrieval systems with an appropriate speed. However, the contents are quite out-dated for 2019.
von KRAug 23, 2018
Need indetail inputs on algorithm usage and correct MeTA assignments with working scripts. That makes learning a complete curve.
von AGOct 14, 2019
Course was well taught the instructor's explanation of the topics was very comprehensive. Overall satisfied with the experience
von THMay 2, 2019
Interesting content\n\nHowever the prof/instructor should practice not pausing so much when explaining the concepts/contents
von HJJun 25, 2016
This course is complemented with a software and text (not mandatory) and good explanation with diagrams...
von HLMay 6, 2018
This advance course just perfect for me who know little bit about advance statistic and linear algebra.
von SMApr 17, 2017
More programming assignments would have helped understanding the topics covered in the course better.
von GAAug 18, 2018
Great at theory and concept explanation... but just few examples and zero programming applications.
von BBDec 10, 2018
One of the best courses I have taken on Coursera. Really liked how the quizzes were structured!
von ASFeb 25, 2020
this course has a lot of ideas but I think these principles need some code for implementing
von DAMay 7, 2018
Arguably the best resource for learning about key concepts of text retrieval online.
von MFFeb 8, 2020
Excellent course, learned a lot that i am already applying to my professional work.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Dieses Kurs ist Teil des reinen Onlineabschlusses Master in Computer Science von University of Illinois at Urbana-Champaign.
Wenn Sie in das komplette Programm aufgenommen werden, werden Ihre Kurse auf Ihren Abschluss angerechnet.
Über University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Über den Spezialisierung Data-Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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