Abstract
Dependency parsing has been a prime focus of NLP research of late due to its ability to help parse languages with a free word order. Dependency parsing has been shown to improve NLP systems in certain languages and in many cases is considered the state of the art in the field.
The use of dependency parsing has mostly been limited to free word order languages, however the usefulness of dependency structures may yield improvements in many of the word’s 6,000+ languages. I will give an overview of the field of dependency parsing while giving my aims for future research.
Many NLP applications rely heavily on the quality of dependency parsing. For this reason, I will examine how different parsers and annotation schemes influence the overall NLP pipeline in regards to machine translation as well as the the baseline parsing accuracy.