Proceedings of the 12th Language Resources and Evaluation Conference
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. Anthology ID: 2020.lrec-1.477 Volume: Proceedings of the 12th Language Resources and Evaluation Conference Month: May Year: 2020 Address: Marseille, France Venue: LREC SIG: Publisher: European Language Resources Association Note: Pages: 3868–3876 Language: English URL: DOI: Bibkey: jonker-etal-2020-bag Copy Citation: BibTeX MODS XML Endnote More options… PDF: = "Bag s performance is compared with that of current state-of-the-art stemming algorithms for the Dutch Language. 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. Abstract 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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