Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents come with excessive lexical noise that makes the inference problem challenging. Secondly, cognitive-semantics are not evident, and important linguistic features are latent in short-text contents. Thirdly, it is hard to measure the correlation between the cognitive short-text semantics and the features pertaining various occupations. We argue that the multi-aspect cognitive features are needed to correctly associate short-text contents to a particular job and discover suitable people for the careers. To this end, we devise a novel framework that on the one hand, can infer short-text contents and exploit cognitive features, and on the other hand, fuses various adopted novel algorithms, such as curve fitting, support vector, and boosting modules to better predict the occupation of the authors. The final estimation module manufactures the $R^w$-tree via coherence weight to tune the best outcome in the inferring process. We conduct comprehensive experiments on real-life Twitter data. The experimental results show that compared to other rivals, our cognitive multi-aspect model can achieve a higher performance in the career estimation procedure, where it is inevitable to neglect the contextual semantics of users.

中文翻译：

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用于推断职业的认知感知短文本理解

利用短文本内容来估计微博作者的职业在许多应用中都有显着的收获。然而挑战比比皆是。首先，简短的文本内容伴随着过多的词汇噪音，这使得推理问题具有挑战性。其次，认知语义不明显，短文本内容中潜藏着重要的语言特征。第三，难以衡量认知短文本语义与各种职业相关特征之间的相关性。我们认为，需要多方面的认知特征才能将短文本内容与特定工作正确关联并发现适合该职业的人。为此，我们设计了一个新颖的框架，一方面可以推断短文本内容并利用认知特征，另一方面，融合了各种采用的新颖算法，例如曲线拟合、支持向量和提升模块，以更好地预测作者的职业。最终估计模块通过相干权重制造 $R^w$-tree 以调整推理过程中的最佳结果。我们对现实生活中的 Twitter 数据进行了全面的实验。实验结果表明，与其他竞争对手相比，我们的认知多方面模型可以在职业评估过程中获得更高的性能，在此过程中不可避免地会忽略用户的上下文语义。我们对现实生活中的 Twitter 数据进行了全面的实验。实验结果表明，与其他竞争对手相比，我们的认知多方面模型可以在职业评估过程中获得更高的性能，在此过程中不可避免地会忽略用户的上下文语义。我们对现实生活中的 Twitter 数据进行了全面的实验。实验结果表明，与其他竞争对手相比，我们的认知多方面模型可以在职业评估过程中获得更高的性能，在此过程中不可避免地会忽略用户的上下文语义。