信息检索杂志(IR)为信息检索的广泛领域中的理论、算法分析和实验的发布提供了一个国际论坛。感兴趣的主题包括对应用程序(例如Web,社交和流媒体,推荐系统和文本档案)的搜索、索引、分析和评估。这包括对搜索中人为因素的研究、桥接人工智能和信息检索以及特定领域的搜索应用程序。 官网地址:https://dblp.uni-trier.de/db/journals/ir/

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From Languages to Information

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《从语言到信息》是一门(半)翻转的课程,有很多在线材料。大部分讲座都有录像,你可以在家里看。每周的测验和编程作业将自动上传和评分EdX提供讲座、测验和家庭作业。网络世界以语言和社交网络的形式存在着大量的非结构化信息。学习如何理解它,以及如何通过语言与人类互动,从回答问题到给出建议。从人类语言文本、语音、网页、社交网络中提取意义、信息和结构。介绍方法(字符串算法、编辑距离、语言建模、机器学习分类器、神经嵌入、倒排索引、协作过滤、PageRank)、应用(聊天机器人、情感分析、信息检索、问答、文本分类、社交网络、推荐系统),以及两者的伦理问题。

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Dan Jurafsky ,人文学科教授,斯坦福大学计算机科学教授兼语言学主席,研究自然语言处理及其在认知和社会科学中的应用。

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Privacy is of worldwide concern regarding activities and processes that include sensitive data. For this reason, many countries and territories have been recently approving regulations controlling the extent to which organizations may exploit data provided by people. Artificial intelligence areas, such as machine learning and natural language processing, have already successfully employed privacy-preserving mechanisms in order to safeguard data privacy in a vast number of applications. Information retrieval (IR) is likewise prone to privacy threats, such as attacks and unintended disclosures of documents and search history, which may cripple the security of users and be penalized by data protection laws. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data. Our contribution is threefold: firstly, we present an overview of privacy threats to IR tasks; secondly, we discuss applicable privacy-preserving mechanisms which may be employed in solutions to restrain privacy hazards; finally, we bring insights on the tradeoffs between privacy preservation and utility performance for IR tasks.

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