Unsupervised Discovery of Negative Categories in Lexicon Bootstrapping
Unsupervised Discovery of Negative Categories in Lexicon Bootstrapping
复制标题
DOI：
--
复制
发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
Tara McIntosh
中科院分区：
文献类型：
--
作者：
Tara McIntosh
开发了多类自举算法以减少语义漂移。通过同时提取多个语义词典，可以限制类别的搜索空间。最好的结果是通过依赖手动制作的负类别来实现的。不幸的是，识别这些类别是不平凡的，它们的使用将无监督的引导范式转移到了有监督的框架上。我们提出了否定的方法，这是自动发现负面类别的第一种方法。 Neg-Finder利用无监督的项聚类，以在引导过程中产生多个负类别。我们的算法有效地消除了手动干预和负面类别的表述的必要性，并且使用域专家定义的负面类别获得了绩效的接近。
Multi-category bootstrapping algorithms were developed to reduce semantic drift. By extracting multiple semantic lexicons simultaneously, a category's search space may be restricted. The best results have been achieved through reliance on manually crafted negative categories. Unfortunately, identifying these categories is non-trivial, and their use shifts the unsupervised bootstrapping paradigm towards a supervised framework. We present NEG-FINDER, the first approach for discovering negative categories automatically. NEG-FINDER exploits unsupervised term clustering to generate multiple negative categories during bootstrapping. Our algorithm effectively removes the necessity of manual intervention and formulation of negative categories, with performance closely approaching that obtained using negative categories defined by a domain expert.