We analyze a few of the commonly used statistics based and machine learning algorithms for natural language disambiguation tasks and observe that they can be re-cast as learning linear separators in the feature space. Each of the methods makes a priori assumptions, which it employs, given the data, when searching for its hypothesis. Nevertheless, as we show, it searches a space that is as rich as the space of all linear separators. We use this to build an argument for a data driven approach which merely searches for a good linear separator in the feature space, without further assumptions on the domain or a specific problem.
We present such an approach - a sparse network of linear separators, utilizing the Winnow learning algorithm - and show how to use it in a variety of ambiguity resolution problems. The learning approach presented is attribute-efficient and, therefore, appropriate for domains having very large number of attributes.
In particular, we present an extensive experimental comparison of our approach with other methods on several well studied lexical disambiguation tasks such as context-sensitive spelling correction, prepositional phrase attachment and part of speech tagging. In all cases we show that our approach either outperforms other methods tried for these tasks or performs comparably to the best.
Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes
Virginia Tech（弗吉尼亚理工大学）；Carnegie Mellon University（卡内基梅隆大学）；Georgia Institute of Technology（佐治亚理工学院）
我们提出了一种方法，同时执行语义分割和介词短语附件决议的字幕图像。语言中的一些歧义如果不同时对相关的图像进行推理就无法解决。如果我们考虑句子“I shot an elephant in my pairs”，只看语言（而不使用常识），我们不清楚是人还是大象穿着睡衣，还是两者兼而有之。我们的方法产生了一组不同的似是而非的假设语义分割和介词短语附件的决议，然后联合重新排名，以选择最一致的对。我们发现，我们的语义分割和介词短语附件决议模块具有互补的优势，联合推理产生更准确的结果比任何模块孤立运行。多个假设也被证明是至关重要的，以改善多模块推理。我们的愿景和语言方法显着优于斯坦福大学解析器（De Marneffe等人，2006）在两个不同的实验中分别提高了17.91%（相对28.69%）和12.83%（相对25.28%）。我们还对DeepLab-CRF进行了小的改进（Chen et al.，2015）。
