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.
제공자:
About this Course
최근 조회 22,132회
학습자 경력 결과
50%
이 강좌를 수료한 후 새로운 경력 시작하기
67%
이 강좌를 통해 확실한 경력상 이점 얻기
29%
급여 인상 또는 승진하기
귀하가 습득할 기술
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
학습자 경력 결과
50%
이 강좌를 수료한 후 새로운 경력 시작하기
67%
이 강좌를 통해 확실한 경력상 이점 얻기
29%
급여 인상 또는 승진하기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 6개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 18시간 필요
영어
자막: 영어, 한국어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
2개 동영상 (총 15분), 6 readings, 2 quizzes
6개의 읽기 자료
Welcome to Text Retrieval and Search Engines!10m
Syllabus10m
About the Discussion Forums10m
Updating your Profile10m
Social Media10m
Course Errata10m
2개 연습문제
Orientation Quiz15m
Pre-Quiz30m
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
6개 동영상 (총 94분), 1 reading, 2 quizzes
6개의 동영상
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
1개의 읽기 자료
Week 1 Overview10m
2개 연습문제
Week 1 Practice Quiz1h
Week 1 Quiz1h
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
6개 동영상 (총 102분), 1 reading, 2 quizzes
6개의 동영상
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
1개의 읽기 자료
Week 2 Overview10m
2개 연습문제
Week 2 Practice Quiz1h
Week 2 Quiz1h
완료하는 데 7시간 필요
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.
완료하는 데 7시간 필요
6개 동영상 (총 75분), 2 readings, 3 quizzes
6개의 동영상
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
2개의 읽기 자료
Week 3 Overview10m
Programming Assignments Overview10m
2개 연습문제
Week 3 Practice Quiz1h
Week 3 Quiz1h
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
7개 동영상 (총 88분), 1 reading, 2 quizzes
7개의 동영상
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
1개의 읽기 자료
Week 4 Overview10m
2개 연습문제
Week 4 Practice Quiz1h
Week 4 Quiz1h
검토
4.4
TEXT RETRIEVAL AND SEARCH ENGINES의 최상위 리뷰
JH 제공Sep 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.
SX 제공Mar 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
WP 제공May 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.
AS 제공Feb 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
LL 제공Nov 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.
AR 제공Sep 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.
PM 제공Aug 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)
GI 제공Aug 20, 2016
Excellent ! Well organized, presented with aptitude to detail. Definitely will recommend and take further units in this specialization.\n\nThanks Prof
석사 학위 취득 시작
This 강좌 is part of the 100% online Master in Computer Science from 일리노이대학교 어버너-섐페인캠퍼스.
If you are admitted to the full program, your courses count towards your degree learning.
일리노이대학교 어버너-섐페인캠퍼스 정보
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.
데이터 마이닝 전문 분야 정보
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.