Abstract
We introduce MaltParser, a data-driven parser generator for dependency parsing. Given a treebank in dependency format, MaltParser can be used to induce a parser for the language of the treebank. MaltParser supports several parsing algorithms and learning algorithms, and allows user-defined feature models, consisting of arbitrary combinations of lexical features, part-of-speech features and dependency features. MaltParser is freely available for research and educational purposes and has been evaluated empirically on Swedish, English, Czech, Danish and Bulgarian.- Anthology ID:
- L06-1084
- Volume:
- Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
- Month:
- May
- Year:
- 2006
- Address:
- Genoa, Italy
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2006/pdf/162_pdf.pdf
- DOI:
- Cite (ACL):
- Joakim Nivre, Johan Hall, and Jens Nilsson. 2006. MaltParser: A Data-Driven Parser-Generator for Dependency Parsing. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).
- Cite (Informal):
- MaltParser: A Data-Driven Parser-Generator for Dependency Parsing (Nivre et al., LREC 2006)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2006/pdf/162_pdf.pdf