论文标题
关于基于链接语法的语言模型的无监督培训
On Unsupervised Training of Link Grammar Based Language Models
论文作者
论文摘要
在此简短说明中，我们探讨了基于链接语法的图形语言模型的无监督培训所需的内容。首先，我们介绍了基于Sleator和Tembyley的链接语法形式主义建立语言模型所需的TER-MANITION标签[21]，并讨论了上下文对链接语法无监督学习的影响。其次，我们将统计链接语法形式主义置于统计链接，从而允许产生统计语言。第三，基于上述形式主义，我们表明Yuret [25]在发现语言关系的经典论文中使用词汇牵引力忽略语言的上下文属性，因此仅依靠依靠Bigrams的无人监督语言学习的方法是有害的。这与基于Yuret Bigram方法的图形语言模型的无监督培训的不可监督的培训非常相关。
In this short note we explore what is needed for the unsupervised training of graph language models based on link grammars. First, we introduce the ter-mination tags formalism required to build a language model based on a link grammar formalism of Sleator and Temperley [21] and discuss the influence of context on the unsupervised learning of link grammars. Second, we pro-pose a statistical link grammar formalism, allowing for statistical language generation. Third, based on the above formalism, we show that the classical dissertation of Yuret [25] on discovery of linguistic relations using lexical at-traction ignores contextual properties of the language, and thus the approach to unsupervised language learning relying just on bigrams is flawed. This correlates well with the unimpressive results in unsupervised training of graph language models based on bigram approach of Yuret.
