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从短会话学习消除歧义的搜索查询

Learning to Disambiguate Search Queries from Short Sessions
课程网址: http://videolectures.net/ecmlpkdd09_mihalkova_ldsqss/  
主讲教师: Lilyana Mihalkova
开课单位: 德克萨斯大学
开课时间: 2009-10-20
课程语种: 英语
中文简介:
网络搜索往往简短而模棱两可。因此,Web查询消歧是一个积极研究的主题并不奇怪。为了向用户提供个性化体验,大多数现有工作依赖于搜索引擎日志数据,其中长时间记录该特定用户以及其他用户的搜索活动。这些方法可能引起隐私问题,并且出于实际原因可能难以实施。我们提出了一种Web查询消歧的方法,该方法仅基于用户搜索活动的短暂一瞥,其平均在4 6次先前搜索的简短会话中捕获。我们的方法利用当前搜索会话与其他用户的先前类似短会话的关系以便预测用户的意图并且基于马尔可夫逻辑,马尔可夫逻辑是已成功应用于过去的挑战性语言问题的统计关系学习模型。我们提出的实证结果证明了我们提出的方法对从商业通用搜索引擎收集的数据的有效性。
课程简介: Web searches tend to be short and ambiguous. It is therefore not surprising that Web query disambiguation is an actively researched topic. To provide a personalized experience for a user, most existing work relies on search engine log data in which the search activities of that particular user, as well as other users, are recorded over long periods of time. Such approaches may raise privacy concerns and may be difficult to implement for pragmatic reasons. We present an approach to Web query disambiguation that bases its predictions only on a short glimpse of user search activity, captured in a brief session of 4-6 previous searches on average. Our method exploits the relations of the current search session to previous similarly short sessions of other users in order to predict the user’s intentions and is based on Markov logic, a statistical relational learning model that has been successfully applied to challenging language problems in the past. We present empirical results that demonstrate the effectiveness of our proposed approach on data collected from a commercial general-purpose search engine.
关 键 词: 网络搜索; 隐私问题; 马尔可夫逻辑
课程来源: 视频讲座网
最后编审: 2020-06-10:yumf
阅读次数: 52