Deep non-probabilistic parsing of large corpora
7581Authors
nan
Abstract
AbstractThis paper reports a large-scale non-probabilistic parsing experiment with a deep LFG parser. We briefly introduce the parser we used, named SXLFG, and the resources that were used together with it. Then we report quantitative results about the parsing of a multi-million word journalistic corpus. We show that we can parse more than 6 million words in less than 12 hours, only 6.7% of all sentences reaching the 1s timeout. This shows that deep large-coverage non-probabilistic parsers can be efficient enough to parse very large corpora in a reasonable amount of time.
Keywords
Full Content
nan
Labeled Text
AbstractThis paper reports a large-scale non-probabilistic parsing experiment with a deep LFG parser. We briefly introduce the parser we used, named SXLFG, and the resources that were used together with it. Then we report quantitative results about the parsing of a multi-million word journalistic corpus. We show that we can parse more than 6 million words in less than 12 hours, only 6.7% of all sentences reaching the 1s timeout. This shows that deep large-coverage non-probabilistic parsers can be efficient enough to parse very large corpora in a reasonable amount of time.
Publication Details
- Paper ID:
- 7581
- Year:
- 2006
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan