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信息检索建模(IRM)

Information Retrieval Modeling (IRM)
课程网址: http://videolectures.net/russir09_hiemstra_irm/  
主讲教师: Djoerd Hiemstra
开课单位: 特温特大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
与例如关系模型是主要的数据库模型的数据库领域的情况不同,不存在诸如信息检索的主导模型或理论。在信息检索中,一些模型适用于某些应用程序,而另一些模型适用于其他应用程序。例如,向量空间模型非常适合在许多(也是非文本的)情形下进行相似性搜索和相关性反馈(如果有一个好的加权函数);如果有相关和非相关文档的例子,概率检索模型或朴素贝叶斯模型可能是一个不错的选择;Google的Pageank模型经常是en用于需要对文档之间的静态关系进行更多建模的情况;区域模型被设计用于在结构化文本中进行搜索;语言模型在需要语言相似性模型或文档先验模型的情况下是有帮助的;在本教程中,我通过展现结果来仔细描述所有这些模型模型假设的CES。我将深入探讨基于统计语言模型的方法。课程结束后,学生可以选择一种适应新情况的信息检索模型,并将其应用于实际情况。
课程简介: There is no such thing as a dominating model or theory of information retrieval, unlike the situation in for instance the area of databases where the relational model is the dominating database model. In information retrieval, some models work for some applications, whereas others work for other applications. For instance, vector space models are well-suited for similarity search and relevance feedback in many (also non-textual) situations if a good weighting function is available; the probabilistic retrieval model or naive Bayes model might be a good choice if examples of relevant and nonrelevant documents are available; Google's Pagerank model is often used in situations that need modelling of more of less static relations between documents; region models have been designed to search in structured text; and language models are helpful in situations that require models of language similarity or document priors; In this tutorial, I carefully describe all these models by exlpaining the consequences of modelling assumptions. I address approaches based on statistical language models in great depth. After the course, students are able to choose a model of information retrieval that is adequate in new situations, and to apply the model in practical situations.
关 键 词: 计算机科学; 信息检索; 建模
课程来源: 视频讲座网
最后编审: 2019-11-18:cwx
阅读次数: 71