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首页> 外文会议>European conference on information retrieval research >Do Your Social Profiles Reveal What Languages You Speak? Language Inference from Social Media Profiles
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Do Your Social Profiles Reveal What Languages You Speak? Language Inference from Social Media Profiles

机译：您的社交资料会显示您说什么语言吗？来自社交媒体资料的语言推断

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In the multilingual World Wide Web, it is critical for Web applications, such as multilingual search engines and targeted international advertisements, to know what languages the user understands. However, online users are often unwilling to make the effort to explicitly provide this information. Additionally, language identification techniques struggle when a user does not use all the languages they know to directly interact with the applications. This work proposes a method of inferring the language(s) online users comprehend by analyzing their social profiles. It is mainly based on the intuition that a user's experiences could imply what languages they know. This is nontrivial, however, as social profiles are usually incomplete, and the languages that are regionally related or similar in vocabulary may share common features; this makes the signals that help to infer language scarce and noisy. This work proposes a language and social relation-based factor graph model to address this problem. To overcome these challenges, it explores external resources to bring in more evidential signals, and exploits the dependency relations between languages as well as social relations between profiles in modeling the problem. Experiments in this work are conducted on a large-scale dataset. The results demonstrate the success of our proposed approach in language inference and show that the proposed framework outperforms several alternative methods.
机译：在多语言的万维网中，对于Web应用程序（例如多语言的搜索引擎和有针对性的国际广告），了解用户能够理解的语言至关重要。但是，在线用户通常不愿意努力明确提供此信息。另外，当用户没有使用他们所知道的所有语言直接与应用程序进行交互时，语言识别技术就会遇到困难。这项工作提出了一种通过分析他们的社交资料来推断在线用户所理解的语言的方法。它主要基于直觉，即用户的体验可能暗示他们所知道的语言。然而，这是不平凡的，因为社会概况通常是不完整的，并且与词汇区域相关或相似的语言可能具有共同的特征;这使得有助于推断语言稀缺和嘈杂的信号。这项工作提出了一种基于语言和社会关系的因子图模型来解决这个问题。为了克服这些挑战，它探索了外部资源以引入更多的证据信号，并在对问题进行建模时利用了语言之间的依赖关系以及配置文件之间的社会关系。这项工作中的实验是在大规模数据集上进行的。结果证明了我们提出的语言推理方法的成功，并表明提出的框架优于几种替代方法。

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