On Unsupervised Training of Link Grammar Based Language Models
This work addresses the challenge of improving unsupervised language learning for computational linguistics, but it appears incremental as it builds on existing link grammar formalisms.
The paper tackles the problem of unsupervised training for link grammar-based language models by introducing termination tags and a statistical link grammar formalism, and critiques Yuret's bigram approach for ignoring contextual properties, leading to unimpressive results.
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.