Constituency Parsing of Bulgarian: Word- vs Class-based Parsing

Masood Ghayoomi, Kiril Simov, Petya Osenova


Abstract
In this paper, we report the obtained results of two constituency parsers trained with BulTreeBank, an HPSG-based treebank for Bulgarian. To reduce the data sparsity problem, we propose using the Brown word clustering to do an off-line clustering and map the words in the treebank to create a class-based treebank. The observations show that when the classes outnumber the POS tags, the results are better. Since this approach adds on another dimension of abstraction (in comparison to the lemma), its coarse-grained representation can be used further for training statistical parsers.
Anthology ID:
L14-1547
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4056–4060
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/696_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/696_Paper.pdf