Parsing Natural Language Sentences by Semi-supervised Methods
This work addresses parsing challenges in computational linguistics, particularly for crosslingual applications, but appears incremental as it builds on existing delexicalized parser transfer methods.
The paper tackled the problem of parsing natural language sentences by developing semi-supervised methods for multi-source crosslingual transfer of delexicalized dependency parsers, resulting in improved parsing performance through a new similarity measure and resource combination method.
We present our work on semi-supervised parsing of natural language sentences, focusing on multi-source crosslingual transfer of delexicalized dependency parsers. We first evaluate the influence of treebank annotation styles on parsing performance, focusing on adposition attachment style. Then, we present KLcpos3, an empirical language similarity measure, designed and tuned for source parser weighting in multi-source delexicalized parser transfer. And finally, we introduce a novel resource combination method, based on interpolation of trained parser models.