Thumbnail
Access Restriction
Open

Author Kulick, Seth ♦ Bies, Ann ♦ Mott, Justin
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Improved Dependency ♦ Phrase Structure Conversion ♦ Encoded Annotation Principle ♦ Automatic Conversion ♦ Phrase-structure Parser ♦ Penn Treebank Guideline ♦ Key Aspect ♦ Comparable Result ♦ Phrase Structure Representation ♦ Part-of-speech Tag ♦ New Approach ♦ Previous Work ♦ Nlp Work ♦ Dependency Representation ♦ Small Number ♦ Underlying Principle
Abstract We investigate the problem of automatically converting from a dependency representation to a phrase structure representation, a key aspect of understanding the relationship between these two representations for NLP work. We implement a new approach to this problem, based on a small number of supertags, along with an encoding of some of the underlying principles of the Penn Treebank guidelines. The resulting system significantly outperforms previous work in such automatic conversion. We also achieve comparable results to a system using a phrase-structure parser for the conversion. A comparison with our system using either the part-of-speech tags or the supertags provides some indication of what the parser is contributing. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study