Name
Abstract | ||
|---|---|---|
Incremental parsing with a context free grammar produces partial syntac- tic structures for an initial fragment on the word-by-word basis. Owing to the syntactic ambiguity, however, too many structures are produced, and therefore its parsing speed becomes very slow. This paper describes a technique for ef- ficient incremental parsing using lexical information. The probability concern- ing dependencies between words, as the lexical information, is automatically ac- quired from a large-scale corpus with syntactic structures. A process for dis- carding syntactic structures which will not be likely has been integrated into the incremental chart parsing. That is, partial syntactic structures whose de- pendency probabilities are not high will be removed from the chart. Our tech- nique proposed in this paper can also be considered as a kind of practical meth- ods of incremental disambiguation. An experiment using Penn Treebank has shown our technique to be feasible and ecien t. |
Year | Venue | Keywords |
|---|---|---|
2001 | NLPRS | context free grammar |
Field | DocType | Citations |
Top-down parsing,S-attributed grammar,Programming language,Computer science,Natural language processing,Artificial intelligence,Parsing | Conference | 1 |
PageRank | References | Authors |
0.37 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takahisa Murase | 1 | 5 | 0.97 |
| Shigeki Matsubara | 2 | 179 | 43.41 |
| Yoshihide Kato | 3 | 22 | 8.15 |
| Yasuyoshi Inagaki | 4 | 243 | 44.27 |