This repository contains an evaluation of the accuracy of open source Finnish part-of-speech taggers and lemmatization algorihtms.

• spaCy 3.3.0
• Experimental Finnish model for spaCy 0.10.0
• FinnPos git commit 81c1f735 (Oct 2019)
• Simplemma 0.6.0
• Stanza 1.4.0
• Trankit 1.1.1
• Turku neural parser pipeline git commit 8c9425dd (Jan 2022)
• UDPipe (through spacy-udpipe 1.0.0)
• UralicNLP 1.3.0
• libvoikko 4.3.1 and Python voikko module 0.5
• Raudikko git commit 572b8104 (Jan 2022)

• FinnTreeBank 1 v1: randomly sampled subset of about 1000 sentences
• FinnTreeBank 2: news, Sofie and Wikipedia subsets
• UD_Finnish-TDT r2.9: the testset

Install dependencies:

• Python 3.9
• libvoikko with Finnish morphology data files
• clang (or other C++ compiler)
• Dependencies needed to compile FinnPos and cg3
• Java 11

Setup git submodules, create a Python 3.9 (must be 3.9 because the Turku parser is incompatible with more recent Python versions) virtual environment and download test data and models by running the following commands:

git submodule init
git submodule update

python3.9 -m venv venv
source venv/bin/activate
pip install wheel
pip install -r requirements.txt

# Compile FinnPos
(cd models/FinnPos/src && make -j 4)

# Compile cg3 in models/cg3
# See https://visl.sdu.dk/cg3/chunked/installation.html

# Compile Raudikko
(cd models/raudikko && ./gradlew shadowJar)

./download_data.sh
./download_models.sh

./run.sh

The numerical results will be saved in results/evaluation.csv, POS and lemma errors made by each model will be saved in results/errorcases, and plots will be saved in results/images.

Execution duration as a function of the F1 score on the concatenated data. Larger values are better on both axes. Notice that the Y-axis is on log scale.

The execution duration is measured as a batched evaluation (a batch contains all sentences from one dataset) on a 4 core CPU. Some methods can be run on a GPU which most likely would improve their performance, but I haven't tested that.

Lemmatization F1 scores for the benchmarked algorithms on the test datasets.

Execution duration as a function of the POS F1 score on the concatenated data.

Note that FinnPos and Voikko do not make a distinction between auxiliary and main verbs and therefore their performance suffers by 4-5% in this evaluation as they mislabel all AUX tags as VERBs.

Part-of-speech F1 scores for the benchmarked algorithms.

Simplemma does not include a POS tagging feature.