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Part of Speech Tagging

This section is concerned with public available Part of Speech (POS) taggers in Danish.

Part-of-speech tagging is the task of classifying words into their part-of-speech, based on both their definition and context. Parts of speech are also known as word classes, which describe the role of the words in sentences relatively to their neighbors (such as verbs and nouns).

Model Train Data License Trained by Tags DaNLP
Flair Danish Dependency Treebank MIT Alexandra Instittut 17 Universal part of speech ✔️
SpaCy Danish Dependency Treebank MIT Alexandra Instittut 17 Universal part of speech ✔️
DaCy Danish Dependency Treebank Apache v2 Center for Humanities Computing Aarhus, K. Enevoldsen 17 Universal part of speech ❌
Stanza Danish Dependency Treebank Apache v2 Stanford NLP Group 17 Universal part of speech ❌

The Danish UD treebank uses 17 universal part of speech tags:

ADJ: Adjective, ADP: Adposition , ADV: Adverb, AUX: Auxiliary verb, CCONJ: Coordinating conjunction, DET: Determiner, INTJ: Interjection, NOUN: Noun, NUM: Numeral, PART: Particle PRON: Pronoun PROPN: Proper noun PUNCT: Punctuation SCONJ: Subordinating conjunction SYM: Symbol VERB: Verb X: Other

Use cases

Part-of-Speech tagging in itself may not be the solution to any particular NLP problem. It is a task that is mostly used as a pre-processing step in order to make it easier for more applicative problems.

It can be used for cleaning/filtering text (e.g. removing punctuation, extracting only nouns) or disambiguation. The language is inherently ambiguous. For instance in Danish, fisk is a verb or a noun. In a sentence such as Fisk en fisk, a part-of-speech tagger is required to understand that the first one is a verb and the second is a noun in such a way that this information can be used in downstream tasks, for example for machine translation (in French it would be two different words depending on whether fisk is a verb -- pêche -- or a noun -- poisson) or for text to speech conversion (a same word can be pronounced differently depending on its meaning).

A medium blog using Part of Speech tagging on Danish, can be found here.

Models

🔧 Flair {#flair}

This project provides a trained part of speech tagging model for Danish using the Flair framework from Zalando, based on the paper Akbik et. al (2018). The model is trained using the data Danish Dependency Treebank and by using FastText word embeddings and Flair contextual word embeddings trained in this project on data from Wikipedia and EuroParl corpus, see here.

The code for training can be found on Flairs GitHub, and the following parameters are set: learning_rate=1, mini_batch_size=32, max_epochs=150, hidden_size=256.

The flair pos tagger can be used by loading it with the load_flair_pos_model method. Please note that the text should be tokenized before hand, this can for example be done using spaCy.

from danlp.models import load_flair_pos_model
from flair.data import Sentence

# Load the POS tagger using the DaNLP wrapper
tagger = load_flair_pos_model()

# Using the flair POS tagger
sentence = Sentence('Jeg hopper på en bil , som er rød sammen med Niels .') 
tagger.predict(sentence) 
print(sentence.to_tagged_string())

# Example
'''Jeg <PRON> hopper <VERB> på <ADP> en <DET> bil <NOUN> , <PUNCT> som <ADP> er <AUX> rød <ADJ> sammen <ADV> med <ADP> Niels <PROPN> . <PUNCT>
'''

🔧 SpaCy {#spacy}

Read more about the spaCy model in the dedicated spaCy docs , it has also been trained using the Danish Dependency Treebank data.

Below is a small getting started snippet for using the spaCy POS tagger:

from danlp.models import load_spacy_model

#Load the POS tagger using the DaNLP wrapper
nlp = load_spacy_model()

# Using the spaCy POS tagger
doc = nlp('Jeg hopper på en bil, som er rød sammen med Niels.')
pred=''
for token in doc:
    pred += '{} <{}> '.format(token.text, token.pos_)
print(pred)

# Example
''' Jeg <PRON> hopper <VERB> på <ADP> en <DET> bil <NOUN> , <PUNCT> som <ADP> er <AUX> rød <ADJ> sammen <ADV> med <ADP> Niels <PROPN> . <PUNCT> 
 '''

DaCy {#dacy}

DaCy is a multi-task transformer trained using SpaCy v. 3. its models is fine-tuned (on DaNE) and based upon the Danish BERT (v2) by botXO and the XLM Roberta large. For more on DaCy see the github repository or the blog post describing the training procedure.

Stanza

Stanza is a python library which provides a neural network pipeline for NLP in many languages. It has been developed by the Stanford NLP Group. The Stanza part-of-speech tagger has been trained on the DDT.

📈 Benchmarks

Accuracy scores are reported below and can be reproduced using pos_benchmarks.py in the example folder, where the details score from each class is calculated.

DaNLP

Model Accuracy Sentences per second (CPU*)
Flair 97.97 ~9
SpaCy 96.15 ~360
DaCy (small) v0.0.0 97.87 ~39
DaCy (medium) v0.0.0 98.27 ~6
DaCy (large) v0.0.0 98.65 ~1
Stanza 97.73 ~34

*Sentences per second is based on a Macbook Pro with Apple M1 chip.

🎓 References