A Novel Approach for Root Selection in the Dependency Parsing
Although syntactic analysis using the sequence labeling method is promising, it can be problematic when the labels sequence does not contain a root label. This can result in errors in the final parse tree when the postprocessing method assumes the first word as the root. In this paper, we present a novel postprocessing method for BERT-based dependency parsing as sequence labeling. Our method leverages the root’s part of speech tag to select a more suitable root for the dependency tree, instead of using the default first token. We conducted experiments on nine dependency treebanks from different languages and domains, and demonstrated that our technique consistently improves the labeled attachment score (LAS) on most of them.
Although syntactic analysis using the sequence labeling method is promising, it can be problematic when the labels sequence does not contain a root label. This can result in errors in the final…
With the advent of pre-trained language models, many natural language processing tasks in various languages have achieved great success.
This paper introduces the first syntactically annotated corpus for Classical Arabic poetry, a morphologically rich ancient Arabic text. The paper describes how the dependency treebank was prepared…