In 2020, the Stanza tool was trained and tested on TalbankenSBX (following MambaDep-style annotation) in order to create a high-quality analysis. Currently (in 2024), this is the default analysis for Swedish in Sparv
Analyscitering
                  
             
          
                
        Språkbanken Text (2022).  sbx-swe-dependency-stanza-stanzasynt (uppdaterad: 2022-08-10). [Analysis]. Språkbanken Text. https://doi.org/10.23695/v6s0-be16
         Ytterligare sätt att citera datamängden.
        Ytterligare sätt att citera datamängden.
    Exempel
This analysis is used with Sparv. Check out Sparv's quick start guide to get started!
To use this analysis, add the following lines under export.annotations in the Sparv corpus configuration file:
- <token>:stanza.dephead_ref  # Sentence-relative positions of the dependency heads
- <token>:stanza.deprel  # Dependency relations to the head
- <token>:stanza.ref  # Token IDs relative to their sentences
For more info on how to use Sparv, check out the Sparv documentation.
Example output:
<token dephead_ref="3" deprel="SS" ref="1">Det</token>
<token dephead_ref="1" deprel="HD" ref="2">här</token>
<token deprel="ROOT" ref="3">är</token>
<token dephead_ref="5" deprel="DT" ref="4">en</token>
<token dephead_ref="3" deprel="SP" ref="5">korpus</token>
<token dephead_ref="3" deprel="IP" ref="6">.</token>
Utvärderingsresultat
A model trained on TalbankenSBX_train and validated on TalbankenSBX_dev yields Labelled Attachment Score of 84.48 on TalbankenSBX_test.
Övriga referenser
- Stanza: Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning. 2020 
- Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In Association for Computational Linguistics (ACL) System Demonstrations. 2020 
- TalbankenSBX: https://spraakbanken.gu.se/en/blog/20200609-the-five-lives-of-talbanken