Learning Semantic Lexicons Using Graph Mutual Reinforcement Based Bootstrapping

这份报纸论述一个方法基于图用一个新引导方法学习语义词典相互的加强(GMR ) 。途径使用仅仅未标记的数据和一些种子词为每个语义范畴学习新词。与另外的引导方法不同，我们使用基于 GMR 的引导排序候选人词和模式。试验性的结果证明基于 GMR 的引导途径在在里面域数据和外面域数据两个都超过存在算法。而且，它证明结果取决于语料库而且质量的尺寸不仅。

This paper presents a method to learn semantic lexicons using a new bootstrapping method based on graph mutual reinforcement （CMR）. The approach uses only unlabeled data and a few seed words to learn new words for each semantic category. Different from other bootstrapping methods, we use GMR-based bootstrapping to sort the candidate words and patterns. Experi- mental results show that the GMR.-based bootstrapping approach outperforms the existing algorithms both in in-domain data and out-domain data. Furthermore, it shows that the result depends on not only the size of the corpus but also the quality.