Name
Title | ||
|---|---|---|
Evaluating impact of re-training a lexical disambiguation model on domain adaptation of an HPSG parser |
Abstract | ||
|---|---|---|
This chapter describes an effective approach to adapting an HPSG parser trained on the Penn Treebank to a biomedical domain.
In this approach, we train probabilities of lexical entry assignments to words in a target domain and then incorporate them
into the original parser. Experimental results show that this method can obtain higher parsing accuracy than previous work
on domain adaptation for parsing the same data. Moreover, the results show that the combination of the proposed method and
the existing method achieves parsing accuracy that is as high as that of an HPSG parser retrained from scratch, but with much
lower training cost. We also evaluated our method on the Brown corpus to show the portability of our approach in another domain.
|
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-90-481-9352-3_15 | Trends in Parsing Technology |
Keywords | Field | DocType |
biomedical domain,target domain,effective approach,existing method,lexical disambiguation model,brown corpus,original parser,hpsg parser,higher parsing accuracy,domain adaptation | Top-down parsing,Computer science,Simple LR parser,Speech recognition,GLR parser,Treebank,Artificial intelligence,Natural language processing,Parsing,Parser combinator,Canonical LR parser,Brown Corpus | Conference |
Volume | ISSN | Citations |
43.0 | 1386-291X | 28 |
PageRank | References | Authors |
1.67 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tadayoshi Hara | 1 | 118 | 9.54 |
| Yusuke Miyao | 2 | 1513 | 125.14 |
| Jun'ichi Tsujii | 3 | 3610 | 232.96 |