Text Retrieval and Search Engines from Coursera

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.

This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern. You will learn the basic concepts, principles, and the major techniques in text retrieval, which is the underlying science of search engines.

What's inside

Syllabus

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.

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.

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.

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.

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.

Week 5

In this week's lessons, you will learn feedback techniques in information retrieval, including the Rocchio feedback method for the vector space model, and a mixture model for feedback with language models. You will also learn how web search engines work, including web crawling, web indexing, and how links between web pages can be leveraged to score web pages.

Week 6

In this week's lessons, you will learn how machine learning can be used to combine multiple scoring factors to optimize ranking of documents in web search (i.e., learning to rank), and learn techniques used in recommender systems (also called filtering systems), including content-based recommendation/filtering and collaborative filtering. You will also have a chance to review the entire course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches natural language processing, a foundation in text-processing

Provides techniques to implement retrieval functions in order to build and operate an IR system

Provides methods and measures for evaluating IR systems

Useful for those interested in search engine technologies

Taught by ChengXiang Zhai, who is recognized for their work in the topic

Provides knowledge for those with interest in topics like machine learning and recommender systems

Reviews summary

Informative course: text retrieval and search engines

According to students, Text Retrieval and Search Engines earns largely positive reviews. The informative lectures mainly focus on theory. Many learners appreciate the engaging assignments and clear explanations. Overall, learners say this course provides a strong introduction to text retrieval for beginners.

Classes delve into the theory behind text retrieval and search engines.

""This course is only based on the theoretical side""

""Lectures are great. However, the assignments are almost impossible to do.""

""One of the worst courses I took ---- the instructor rarely gets down and into to the gist of a topic""

The instructor presents the material in a clear and easy-to-understand manner.

""Very good course!! Just a problem with the MeTa platform, maybe it's time to change that?""

""Cheng is a very good teacher. I'm amazed at how clearly he can teach complex topics.""

""this course helps me to best understand the concepts of text retrieval.""

Learners find the assignments to be engaging and helpful for understanding the material.

""Great class with a nice mix of theoretical and practical lessons.""

""There was a competition at the end of the course which pushed us to come up with new ideas.""

""Tips: The quiz is not necessarily easy, but easy to pass, if that's what you care about. However, the nitty-gritty of this course is better comprehended by implementing it on some tasks.""

The course provides a wealth of knowledge on text retrieval and search engines.

""very very very Informative""

""That was the best course !!!""

""Very good and comprehensive MOOC course.""

Some learners express concerns about the course content being outdated.

""The course topic is very interesting but i found some difficulties to follow up some lectures."

""Class is not updated, so the programming assignments no longer work without a lot of effort.""

""The programming assignment is broken in many, many ways.""

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Text Retrieval and Search Engines with these activities:

Review 'Introduction to Information Retrieval' by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze

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Reinforces the foundational concepts and principles of information retrieval covered in the course.

View Introduction to Information Retrieval on Amazon

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Read through each chapter thoroughly, taking notes on key concepts and principles.
Complete the practice exercises at the end of each chapter to test your understanding.
Summarize the main ideas of each chapter in your own words.

Organize and review course notes, assignments, and quizzes

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Improves retention and understanding of course material by organizing and reviewing it.

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Gather all course notes, assignments, and quizzes into one place.
Review the materials regularly, highlighting important concepts and making notes.
Summarize the main ideas of each topic and create study guides.

Solve practice problems on information retrieval topics

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Strengthens understanding of information retrieval concepts through repetitive exercises.

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Identify online practice problems or textbooks with exercises on information retrieval topics.
Work through the problems, checking your answers against provided solutions.
Review the solutions and identify areas where you need further practice.

Four other activities

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Form a study group with other students taking the course

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Enhances understanding of course material through peer discussions and collaboration.

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Find other students in the course and form a study group of 3-5 people.
Meet regularly to discuss course topics, review lecture materials, and work on assignments together.

Build a simple search engine for a small text collection

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Provides hands-on experience in applying the concepts of information retrieval to a practical problem.

Browse courses on Vector Space Model

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Gather a small text collection (e.g., a set of news articles or blog posts).
Preprocess the text collection by removing stop words, stemming words, and building an inverted index.
Implement a basic search engine using the vector space model.
Evaluate the performance of your search engine using metrics such as precision and recall.

Attend a workshop on information retrieval organized by a professional organization

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Provides exposure to cutting-edge research and industry trends in information retrieval.

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Identify professional organizations that organize workshops on information retrieval.
Research upcoming workshops and select one that aligns with your interests.
Attend the workshop, take notes, and engage in discussions with experts and peers.

Follow online tutorials on advanced information retrieval techniques

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Expands knowledge of information retrieval techniques beyond the scope of the course.

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Identify online tutorials or courses covering advanced information retrieval techniques.
Work through the tutorials, taking notes and completing any exercises.
Apply the techniques learned to the course assignments and projects.

Career center

Learners who complete Text Retrieval and Search Engines will develop knowledge and skills that may be useful to these careers:

Search Engine Evaluator

As a Search Engine Evaluator, your role is typically to review and assess the performance of search engines. Knowledge in text retrieval and search engine fundamentals is a major plus and would help you perform better in this role. This course provides a great starting point or refresher on the fundamentals of text retrieval models, search engines, and evaluation techniques. With the knowledge from this course, you can potentially become a top-notch Search Engine Evaluator.

See salaries and explore the career path for Search Engine Evaluator

Data Analyst

A large part of a Data Analyst's routine is spent collecting, cleaning and analyzing data, to reach a particular conclusion or solve a particular problem. This usually involves searching for relevant data and this course will help you perform better in this role by laying the foundation for you to understand the principles of text retrieval and search engines. With knowledge of search engine technologies, you will be able to efficiently find the information required to perform your analysis and evaluation.

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Information Architect

Information Architects typically devise strategies for structuring, organizing and labeling web sites, intranets, online communities and software applications. This course on Text Retrieval and Search Engines can help you build a foundation in the key concepts and techniques used in search.

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Software Engineer

As a Software Engineer, you work with programming and computer science. This course will help strengthen your knowledge of text retrieval models, search engines, and evaluation techniques. While some roles may not require this directly, the fundamentals covered can help with your day-to-day tasks.

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Web Developer

Web Developers focus on the design and development of websites. As a Web Developer, it is crucial to understand how search engines work, which is covered in this course. With this background, you can design and develop websites that are easily discoverable and rank well in search engine results.

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UX Researcher

User Experience (UX) Researchers study user behavior to improve the usability and overall experience of a product or service. This typically involves collecting and analyzing user feedback. This course provides a great introduction to search engine evaluation techniques, which can be applied to analyzing user feedback from search and helping to improve the overall user experience.

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Information Scientist

Information Scientists typically gather, analyze, and interpret data from a variety of sources. The data can be structured or unstructured, and it can come from a variety of sources, such as surveys, interviews, and social media. This course builds a good knowledge foundation for Information Scientists by providing an overview of search engine technologies and techniques.

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Content Strategist

Content Strategists are responsible for planning, developing, and managing content for a variety of media. As a Content Strategist, it's critical to understand user behavior, which involves understanding how users search for and interact with content online. This course can help build a foundation for understanding these concepts by introducing search engine technologies and the principles of text retrieval.

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Digital Marketing Manager

Digital Marketing Managers are responsible for developing and executing marketing campaigns across a range of digital channels, including search engines. As a Digital Marketing Manager, knowledge of search engine optimization (SEO) is critical and understanding how search engines work, which is covered extensively in this course, provides a solid foundation for a successful career in digital marketing.

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Product Manager

Product Managers are responsible for managing and developing products. As a Product Manager, you need to understand the needs of your users. This course introduces search engine technologies, information retrieval, and evaluation techniques which can help you better understand your users, their needs, and optimize your product accordingly.

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Technical Writer

Technical Writers create user manuals, help files, and other documentation for software and hardware products. Having a strong understanding of search engine technologies and text retrieval principles is not always a direct requirement for Technical Writers, but can help you create user manuals, help files and other documentation that is easy to find, understand, and use.

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Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This often involves collecting and analyzing data from a variety of sources. This course provides a foundation in search engine technologies and techniques, which can be applied to collecting and analyzing data from the web.

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Librarian

Librarians typically help people find and access information. Since information retrieval is the underlying science of search engines and this course covers this in depth, it is a great way to gain a strong understanding of the fundamentals.

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Market Researcher

Market Researchers typically conduct surveys, interviews, and other research to collect data about consumer behavior. This course on Text Retrieval and Search Engines may be useful for Market Researchers as it teaches techniques for collecting and analyzing data from the web.

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Customer Service Representative

Customer Service Representatives typically provide support to customers who have questions or problems with a product or service. As a Customer Service Representative, understanding how search engines work can help provide better support to customers.

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