Published July 8, 2020
| Version v1
Publication
A comparative study of classifier combination applied to NLP tasks
Description
The paper is devoted to a comparative study of classifier combination methods, which have been successfully
applied to multiple tasks including Natural Language Processing (NLP) tasks. There is variety of classifier
combination techniques and the major difficulty is to choose one that is the best fit for a particular
task. In our study we explored the performance of a number of combination methods such as voting,
Bayesian merging, behavior knowledge space, bagging, stacking, feature sub-spacing and cascading, for
the part-of-speech tagging task using nine corpora in five languages. The results show that some methods
that, currently, are not very popular could demonstrate much better performance. In addition, we learned
how the corpus size and quality influence the combination methods performance. We also provide the
results of applying the classifier combination methods to the other NLP tasks, such as name entity recognition
and chunking. We believe that our study is the most exhaustive comparison made with combination
methods applied to NLP tasks so far.
Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/99062
- URN
- urn:oai:idus.us.es:11441/99062