Name
Title | ||
|---|---|---|
Using supertags and encoded annotation principles for improved dependency to phrase structure conversion |
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. |
Year | Venue | Keywords |
|---|---|---|
2012 | HLT-NAACL | resulting system,phrase-structure parser,encoded annotation principle,dependency representation,phrase structure conversion,phrase structure representation,comparable result,previous work,penn treebank guideline,automatic conversion,improved dependency,key aspect,nlp work |
Field | DocType | Citations |
Annotation,Computer science,Speech recognition,Phrase structure rules,Treebank,Artificial intelligence,Natural language processing,Parsing,Encoding (memory) | Conference | 1 |
PageRank | References | Authors |
0.39 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seth Kulick | 1 | 221 | 29.66 |
| Ann Bies | 2 | 136 | 20.02 |
| Justin Mott | 3 | 27 | 4.93 |