The stemming module's performance is compared with that of current state-of-the-art stemming algorithms for the Dutch Language. The tagging module is developed and evaluated using three algorithms: Multinomial Logistic Regression (MLR), Neural Network (NN) and Extreme Gradient Boosting (XGB). Our algorithm combines a new tagging module with a stemmer that uses tag-specific sets of rigid rules: the Bag & Tag'em (BT) algorithm. The main issue is that most current stemmers cannot handle 3rd person singular forms of verbs and many irregular words and conjugations, unless a (nearly) brute-force approach is used. Ībstract = "We propose a novel stemming algorithm that is both robust and accurate compared to state-of-the-art solutions, yet addresses several of the problems that current stemmers face in the Dutch language. The code and data used for this paper can be found at.
Even though there is still room for improvement, the new BT algorithm performs well in the sense that it is more accurate than the current stemmers and faster than brute-force-like algorithms.
The main issue is that most current stemmers cannot handle 3 rd person singular forms of verbs and many irregular words and conjugations, unless a (nearly) brute-force approach is used. We propose a novel stemming algorithm that is both robust and accurate compared to state-of-the-art solutions, yet addresses several of the problems that current stemmers face in the Dutch language.
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