Would it be possible to run some benchmarks so we know the performance
difference between the two?
The OpenNLP POS Tagger can be customized, currently is possible to
replace the feature generation,
it can probably be optimized for the medical domain, the default feature
generation is tuned for the news domain.
Replacing the learning algorithm is currently not possible, but we will
work on that for the next release.
Do you use a tag dictionary? Maybe it is possible to generate something
from the existing dictionaries already
used by cTAKES.
Jörn
On 04/08/2013 06:15 PM, Chen, Pei wrote:
Hi,
While working on the Dependency Parser/SRL labeler, we also have a POSTagger
from ClearNLP. It is fairly simple and I have the code ready (also trained on
the same data as the dep parser- MiPaq/SHARP) to be checked-in. What does the
folks think:
We can include both Analysis Engines in the ctakes-pos-tagger project. But
should we leave the current OpenNLP in the default pipeline or default to the
latest?
"The ClearNLP POS tagger shows more robust results on unknown words by
generalizing lexical features. You can find the reference from this paper.
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection, Jinho D. Choi,
Martha Palmer, Proceedings of the 50th Annual Meeting of the Association for
Computational Linguistics (ACL'12), 363-367, Jeju, Korea, 2012. [1] It also uses
AdaGrad for machine learning, which is a more advanced learning algorithm than
maximum entropy used by OpenNLP."
[1] http://aclweb.org/anthology-new/P/P12/P12-2071.pdf
