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User Modelling for News Web Sites with Word Sense Based Techniques

机译：使用基于词义的技术对新闻网站进行用户建模

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摘要

SiteIF is a personal agent for a bilingual news web site that learns user's interests from the requested pages. In this paper we propose to use a word sense based document representation as a starting point to build a model of the user's interests. Documents passed over are processed and relevant senses (disambiguated over WordNet) are extracted and then combined to form a semantic network. A filtering procedure dynamically predicts new documents on the basis of the semantic network. There are two main advantages of a sense-based approach: first, the model predictions, being based on senses rather than words, are more accurate; second, the model is language independent, allowing navigation in multilingual sites. We report the results of a comparative experiment that has been carried out to give a quantitative estimation of these improvements.
机译：SiteIF是双语新闻网站的个人代理，可以从请求的页面中了解用户的兴趣。在本文中，我们建议以基于词义的文档表示为起点来构建用户兴趣模型。处理传递过来的文档，提取相关含义（通过WordNet消除歧义），然后组合形成语义网络。过滤过程基于语义网络动态预测新文档。基于感觉的方法有两个主要优点：首先，基于感觉而不是单词的模型预测更准确；其次，该模型与语言无关，可以在多语言站点中导航。我们报告了一个比较实验的结果，该实验已经进行了定量评估。

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