分类:用于文章主题识别等任务的自然语言处理技术

分类是很多研究的基础。例如，从对每一篇文章的了解过渡到对整个领域或者某个分支领域的了解，需要做粗粒化，需要做文章主题识别。例如，每一个领域的平均引用次数不一样，如果需要实现被引次数在不同领域之间比较，就需要转化一下一个“货币”，而这个转化的基础就是一个好的领域识别。

这个识别工作主要的思想是利用文章引用网络得到网络相似性，结合文章的标题摘要甚至全文来判断文章得到的语义相似性，然后运用相似性来做某种聚类。具体方法上思考word2vec和LDA的结合。

LDA(Latent Dirichlet allocation)有几个关键点：[math]\displaystyle{ P\left(w|d\right)=\sum_{t}P\left(w|t\right)P\left(t|d\right) }[/math]，[math]\displaystyle{ P\left(w|t\right) }[/math]来自于整个语料库，分布函数的具体的Dirichlet函数形式。我现在希望构建一个无参数的不用具体函数形式的假设的主题发现方法。可以是自洽的迭代算法，例如从某一组假定的[math]\displaystyle{ P\left(w|t\right) }[/math]开始，求出来[math]\displaystyle{ P\left(t|d\right) }[/math]，接着再次更新[math]\displaystyle{ P\left(w|t\right) }[/math]。也可以是某一个目标下的优化算法，例如这样的目标，对于每一个文档[math]\displaystyle{ d }[/math]都满足[math]\displaystyle{ P_{em}\left(w|d\right)-\sum_{t}P\left(w|t\right)P\left(t|d\right) }[/math]。

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