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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/text-retrieval
课程评论：没有评论
课程概述：文本检索与搜索引擎 近年来，自然语言文本数据（包括网页、新闻文章、科学文献、电子邮件、企业文档以及社交媒体如博客文章、论坛帖子、产品评论和推文）的急剧增长，使得文本数据在发现人们的意见和偏好等方面变得极为重要。本课程将探讨搜索引擎技术，它们在涉及文本数据的数据挖掘应用中发挥着重要作用。搜索引擎可以快速从大量文本数据中发现相关的子集，并帮助分析人员解释在数据中发现的模式。本课程将介绍文本检索的基本概念、原理及主要技术，揭示搜索引擎背后的科学。 课程大纲： 第1周：自然语言处理技术基础，检索模型概念，以及向量空间模型的基本思想。 第2周：详细了解向量空间模型的工作原理，设计文档检索排名函数的主要启发式方法，以及如何实施信息检索系统（即搜索引擎），包括如何构建倒排索引和快速评分文档。 第3周：学习如何评估信息检索系统，了解评估检索结果集的基本指标，以及评估排名列表的主要指标，如平均精度（AP）和标准化折扣累计增益（nDCG），以及评估中的实际问题，如统计显著性测试和池化。 第4周：理解概率检索模型和统计语言模型，特别是查询似然检索函数的细节及两种特定平滑方法，并学习查询似然检索函数如何与向量空间模型中的检索启发式相连。 第5周：学习信息检索中的反馈技术，包括向量空间模型的Rocchio反馈方法和语言模型的混合模型反馈。同时，了解网络搜索引擎的工作原理，包括网页爬虫、网页索引以及如何利用网页之间的链接对网页进行评分。 第6周：学习如何将机器学习应用于结合多种评分因素以优化网页搜索中的文档排名（即学习排名），并了解推荐系统中的技术（也称为过滤系统），包括基于内容的推荐/过滤和协同过滤。此外，您将有机会回顾整个课程内容。
Part: 1
Title:Week 1
Description: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.
Part: 2
Title:Week 2
Description: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.
Part: 3
Title:Week 3
Description: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.
Part: 4
Title:Week 4
Description: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.
Part: 5
Title:Week 5
Description: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.
Part: 6
Title:Week 6
Description: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.
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.
