Abstract
We present a method to discriminate between texts written in either the Netherlandic
or the Flemish variant of the Dutch language. The method draws on a feature
bundle representing text statistics, syntactic features, and word n-grams. Text
statistics include average word length and sentence length, while syntactic features
include ratios of function words and part-of-speech n-grams. The effectiveness of
the classifier was measured by classifying Dutch subtitles developed for either
Dutch or Flemish television. Several machine learning algorithms were compared
as well as feature combination methods in order to find the optimal generalization
performance. A machine-learning meta classifier based on AdaBoost attained the
best F-score of 0.92.
or the Flemish variant of the Dutch language. The method draws on a feature
bundle representing text statistics, syntactic features, and word n-grams. Text
statistics include average word length and sentence length, while syntactic features
include ratios of function words and part-of-speech n-grams. The effectiveness of
the classifier was measured by classifying Dutch subtitles developed for either
Dutch or Flemish television. Several machine learning algorithms were compared
as well as feature combination methods in order to find the optimal generalization
performance. A machine-learning meta classifier based on AdaBoost attained the
best F-score of 0.92.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial) |
| Pages | 190-199 |
| Publication status | Published - 3 Apr 2017 |
| Externally published | Yes |