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POS tagging, lemmatization and dependency parsing of West Frisian

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Wilbert Heeringa, Gosse Bouma, Martha Hofman, Eduard Drenth, Jan Wijffels, Hans Van de Velde

Computation and Language · Submitted 16 Jul 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim A significant improvement was found for `lemma'.

how it is stated · asserted 1.00 read the record →

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arXiv ID2107.07974 (version 2)
Pith IDJUKLNBXG, Pith's permanent record of this paper and its review
CitationEduard Drenth, Gosse Bouma, Hans Van de Velde, Jan Wijffels, Martha Hofman, Wilbert Heeringa. "POS tagging, lemmatization and dependency parsing of West Frisian." arXiv:2107.07974 (2021). https://pith.science/paper/JUKLNBXG
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,600 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.24 · human participants 0.06 · controlled comparison 0.86 · randomized assignment 0.02 · states quantitative result 0.70 · compares with prior results 0.90 · mentions code or data availability 0.89.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We present a lemmatizer/POS-tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependency version 2. POS tags were assigned to words by using a Dutch POS tagger that was applied to a literal word-by-word translation, or to sentences of a Dutch parallel text. Best results were obtained when using literal translations that were created by using the Frisian translation program Oersetter. Morphologic and syntactic annotations were generated on the basis of a literal Dutch translation as well. The performance of the lemmatizer/tagger/annotator when it was trained using default parameters was compared to the performance that was obtained when using the parameter values that were used for training the LassySmall UD 2.5 corpus. A significant improvement was found for `lemma'. The Frisian lemmatizer/PoS tagger/dependency parser is released as a web app and as a web service.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a lemmatizer/POS-tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependency version 2. contribution 0.97 0.94 0.12 asserted 1.00
s1 POS tags were assigned to words by using a Dutch POS tagger that was applied to a literal word-by-word translation, or to sentences of a Dutch parallel text. method 1.00 0.79 0.04 asserted 1.00
s2 Best results were obtained when using literal translations that were created by using the Frisian translation program Oersetter. result 0.88 0.87 0.17 asserted 1.00
s3 Morphologic and syntactic annotations were generated on the basis of a literal Dutch translation as well. method 0.99 0.81 0.04 asserted 1.00
s4 The performance of the lemmatizer/tagger/annotator when it was trained using default parameters was compared to the performance that was obtained when using the parameter values that were used for training the LassySmall UD 2.5 corpus. method 0.95 0.93 0.05 asserted 0.88
s5 A significant improvement was found for `lemma'. result 1.00 0.95 0.24 asserted 1.00
s6 The Frisian lemmatizer/PoS tagger/dependency parser is released as a web app and as a web service. contribution 0.98 0.84 0.12 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.72.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a lemmatizer/POS-tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependency version 2. POS tags were assigned to words by using a Dutch POS tagger that was applied to a literal word-by-word translation, or to sentences of a Dutch parallel text. Best results were obtained when using literal translations that were created by using the Frisian translation program Oersetter. Morphologic and syntactic annotations were generated on the basis of a literal Dutch translation as well. The performance of the lemmatizer/tagger/annotator when it was trained using default parameters was compared to the performance that was obtained when using the parameter values that were used for training the LassySmall UD 2.5 corpus. A significant improvement was found for `lemma'. The Frisian lemmatizer/PoS tagger/dependency parser is released as a web app and as a web service.

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