Learning Part of Speech Disambiguation Rules for Swedish

Application domain Part-of-Speech Tagging for Swedish
Source The Stockholm-Umea. Corpus
Dataset size Over 100.000 training examples extracted from the corpus that consists of one million tagged words
Data format PROGOL
Systems used PROGOL 4.2
References see [1]
Pointers http://www.dsv.su.se/ML/TFR/ilp.html

Rules eliminating faulty tags have been induced using Progol on the Stockholm-Umecl corpus. In previously reported experiments, almost no linguistically motivated background knowledge was used. Still, the result was rather promising (recall 97.7%, with a pending average ambiguity of 1.13 tags/word). Compared to the previous study, a much richer, more linguistically motivated, background knowledge has been supplied in this task, consistmg of examples of noun phrases, verb chains, auxiliary verbs, and sets of part of speech categories. The aim has been to create the background knowledge rapidly, without laborious hand-coding of linguistic knowledge. In addition to the new background knowledge, new, more expressive rule types have been induced for two part of speech categories and compared to the corresponding rules of the previous bottom-Iine experiment. The new rules perform considerably better, with a recall of 99.4% for the new rules, compared to 97.6% for the old rules. Precision was slightly better for the new rules.


  1. Lindberg N. and Eineborg I., "Improving part of speech disambiguation rules by adding linguistic knowledge". In Proceedings of the Ninth International Workshop on Inductive Logic Programming, LNAI Series 1634, Springer (1999).

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