Creating POS Tagging and Dependency Parsing Experts via Topic Modeling

Atreyee Mukherjee, Sandra Kübler, Matthias Scheutz


Abstract
Part of speech (POS) taggers and dependency parsers tend to work well on homogeneous datasets but their performance suffers on datasets containing data from different genres. In our current work, we investigate how to create POS tagging and dependency parsing experts for heterogeneous data by employing topic modeling. We create topic models (using Latent Dirichlet Allocation) to determine genres from a heterogeneous dataset and then train an expert for each of the genres. Our results show that the topic modeling experts reach substantial improvements when compared to the general versions. For dependency parsing, the improvement reaches 2 percent points over the full training baseline when we use two topics.
Anthology ID:
E17-1033
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
347–355
Language:
URL:
https://www.aclweb.org/anthology/E17-1033
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1033.pdf