Slovenska raziskovalna infrastruktura za jezikovne vire in tehnologije
Common Language Resources and Technology Infrastructure, Slovenia

FAQ for Slovene language resources and technologies

This FAQ is part of the documentation of the CLASSLA CLARIN knowledge centre for South Slavic languages. If you notice any missing or wrong information, please do let us know on, Subject “FAQ_Slovene”.

The questions in this FAQ are organised into three main sections:

1. Online Slovene language resources

Q1.1: Where can I find Slovene dictionaries?

Below we list the main dictionary portals offered by CLARIN.SI partners or supported by CLARIN.SI:

Dictionaries by other providers:

  • Evroterm, multilingual terminology database, and a list of on-line dictionaries, by the Government of the Republic of Slovenia;
  • Islovar, a terminological dictionary for the field of Informatics by the Slovene Society for Informatics;
  • Wikislovar, the Slovene Wiktionary, i.e., the multilingual, openly accessible and openly editable dictionary.

Q1.2: How can I analyse Slovene corpora online?

CLARIN.SI offers access to four concordancers, which share the same set of corpora and back-end, but have different front-ends:

  • CLARIN.SI Crystal noSketch Engine, an open-source variant of the well-known Sketch Engine.  Instructions for its use are available here. CLARIN.SI offers two installations of Crystal noSketch Engine: an open installation (no log-in, which simplifies use for less advanced users) and a version with log-in which allows subcorpus creation and personalised display of e.g. corpus attributes.
  • KonText, with a somewhat different user interface. Basic functionality is provided without logging in, but to use more advanced functionalities, it is necessary to log in via your home institution.
  • CLARIN.SI Bonito noSketch Engine is the old version of noSketch Engine with a radically different user interface from Crystal. This version offers some functions that the new noSketch Engine does not, in particular, accessing the results of queries in XML, where it is enough to add the parameter “format=XML” to the end of the query URL.

Documentation on how to query corpora via the SketchEngine-like interfaces is available here.

Note that the commercial Sketch Engine also offers access to several Slovene language corpora, as well as some additional tools that are not accessible on the free NoSketch Engine, including the tools to analyse collocations (Word sketches), synonyms and antonyms (Thesaurus), the tools to compute frequency lists of multiword expressions (N-grams) and to extract keywords and terms. It also allows users to create their own corpora.

Some Slovene corpora, esp. those produced in the scope of the “Communication in Slovene” project have their specialised web concordancers, cf. the corpora listed in Q1.3.

Q1.3: Which Slovene corpora can I analyse online?

The main reference corpus for Slovene is Gigafida (1 billion words), which you can query via its specialized interface, via Crystal noSkE, Bonito noSkE or KonText. Note that the corpus is also available in a version which has (near) duplicate paragraphs removed, cf. noSkE or KonText. A balanced subset of Gigafida is KRES (100 million tokens), which you can query via its specialized interface.

For a complete list of corpora available under CLARIN.SI concordancers, see the index for Crystal noSkE, Bonito noSkE or KonText. Below we list some of the important ones, with links to the Crystal noSketch Engine concordancer:

  • general language corpora (apart from Gigafida) are and slWaC, large corpora (2 billion and 900 million tokens respectively) of Slovene texts from the Web
  • specialized corpora include the corpus of academic writing KAS, the corpus of scientific publications from the Open Science portal OSS, the corpus of scientific texts of contemporary Slovenian KZB, the corpus of user-generated content (blogs, forums, comments, and tweets) Janes, the monitor news corpus Trendi, the spoken corpus GOS, the parliamentary corpora siParl, ParlaMint-SI, yu1Parl and the Carniolan Provincial Assembly corpus Kranjska, the Wikipedia corpus CLASSLAWiki-sl, the corpus of historical Slovene IMP, the corpus of Slovenian periodicals (1771-1914) sPeriodika, the Proverbs corpus, the corpus of longer narrative prose KDSP, the corpus of youth literature MAKS, the developmental corpus ŠOLAR, and the corpus of Slovene as a foreign language KOST
  • manually annotated corpora include the reference training corpus SUK, the corpus of historical Slovene goo300k (sampled from the IMP corpus), the corpus of term-annotated texts RSDO5, and the corpora of user-generated content Janes Norm (sampled from the Janes corpus), which is manually annotated with normalised word-forms, and Janes Tag (sampled from Janes-norm), also manually annotated with morphosyntactic descriptions, lemmas, and named entities
  • a meta corpus metaFida, which contains 6 billion tokens, unites the most important publicly accessible Slovene corpora and enables a uniform search through them
  • parallel corpora include the multilingual European parliamentary corpora ParlaMint-XX, paired with the machine-translated English corpora ParlaMint-XX-en, multilingual DGT translation memory corpus EU DGT-UD: Slovenian, the Slovene-English corpus TRANS5, the Italian-Slovene corpus ISPAC, the French-Slovene corpus LeMonde, and the Japanese-Slovene corpus jaSlo

Furthermore, the commercial Sketch Engine includes the following Slovene corpora: learner corpus of proofread and translations Lektor, which you can also query via its specialized interface, EUR-Lex Slovenian 2/2016, parallel corpus EUROPARL7, created from the European Parliament Proceedings, and OPUS2, a parallel corpus of 40 languages.

Q1.4: What linguistic annotation schemas are used in Slovene corpora?

For a detailed overview of annotation schemas, see the information on linguistic annotation of Slovene corpora on the CJVT Wiki. The overview covers the following corpus annotation levels: tokenisation, sentence segmentation, lemmatisation, and JOS/MULTEXT-East morphosyntactic descriptions, JOS syntax, Universal Dependencies (UD) syntax, semantic role labelling (SRL), named entities (NER), normalizationcoreferences, and relations. It also includes specialised systems for annotating language corrections in the Šolar (Slovene student texts) and KOST (texts by speakers of Slovene as a foreign language) corpora. The section on each annotation level contains an introduction, explanation of tags or processes, annotation guidelines, and relevant references and links.

Most of Slovene corpora are annotated according to the MULTEXT-East morphosyntactic specifications for Slovene. On the level of syntax, and esp. for older corpora, the Slovene-specific SSJ annotation scheme is used. Corpora are also annotated according to the Universal Dependencies guidelines. Named entities are often annotated following the Janes NE guidelines for Slovene.

Q1.5: Where can I download Slovene resources?

The main point for archiving and downloading Slovene language resources is the repository of CLARIN.SI.

In addition to the resources mentioned above and below, the repository offers:

2. Tools to annotate Slovene texts

Q2.1: How can I perform basic linguistic processing of my Slovene texts?

The state-of-the-art CLASSLA pipeline provides processing of standard and non-standard (Internet) Slovene on the levels of tokenization and sentence splitting, part-of-speech tagging, lemmatisation, dependency parsing and named entity recognition. The CLASSLA pipeline uses two tokenizers: rule-based tokenizer Obeliks4J for Slovene standard language pipeline and reldi-tokeniser for other cases. There are also available off-the-shelf models for lemmatisation of standard and non-standard Slovene, part-of-speech tagging of standard and non-standard Slovene, and semantic role labeling of standard Slovene. You can try out the pipeline at the CLASSLA Annotation tool website.

The documentation for the installation and use of the pipeline is available here. Furthermore, the CLASSLA pipeline offers some additional features, namely the usage of Slovene-specific dependency parsing system, inflectional lexicon and pretokenized data, which are documented here.

In addition to this, tokenisation, part-of-speech tagging, and lemmatisation are provided by a CLARIN.SI service ReLDIanno as well. The documentation for using the service is available here. It can be used via a web interface or as a web service. You can also install the same tools locally, namely the tokenizer and part-of-speech tagger and lemmatiser.

Q2.2: How can I standardize my texts prior to further processing?

Currently, the only text on-line normalisation tool available through the CLARIN.SI services (ReLDIanno) is the REDI diacritic restorer. Its usage is documented here. You can also download the REDI diacritic restorer, install it and use it locally.

For word-level normalisation of e.g. historical and user-generated Slovene texts, you can download and install the CSMTiser text normaliser.

Q2.3: How can I annotate my texts for named entities?

Named entity recognition is provided by the CLASSLA pipeline, which also offers off-the-shelf models for standard and non-standard Slovene. In addition to this, on-line NER is available via the CLARIN.SI service ReLDIanno. You can also download the janes-ner tool.

Q2.4: How can I syntactically parse my texts?

You can syntactically parse Slovene texts in multiple ways:

3. Datasets to train Slovene annotation tools

Q3.1: Where can I get word embeddings or pre-trained language models for Slovene?

  • The embeddings trained on the largest collection of Slovene textual data (Gigafida, slWaC, JANES, KAS, MaCoCu-sl, etc.) is the embedding collection.
  • There are also collections of trained embeddings for Slovene available from SketchEngine and from fastText.
  • If you want to train your own embeddings, the largest freely available collection of Slovene texts is the Slovene portion of Commoncrawl.

You can also use the Slovene BERT/RoBERTa model SloBERTa, a state-of-the-art model representing words/tokens as contextually dependent word embeddings. It allows you to extract word embeddings for every word occurrence, which can then be used in training a model for an end task. The scripts and programs used for data preparation and training the model are available here.

Q3.2: What data is available for training a text normaliser for Slovene?

For training text normalisers for Internet Slovene, the Janes-norm dataset can be used. For normalising historical data, the goo300k dataset should be used.

Q3.3: What data is available for training a part-of-speech tagger for Slovene?

The reference dataset for training a standard tagger is SUK. There is also a silver-standard dataset available, jos1M. There are also training datasets available for Internet Slovene (Janes-tag) and for historical Slovene (goo300k).

You can also use the CLASSLA pipeline in combination with the CLARIN.SI embeddings and the training dataset SUK to train and evaluate your own part-of-speech tagger. The documentation is available here.

Q3.4: What data is available for training a lemmatiser for Slovene?

Lemmatisers can be trained either on the tagger training data (SUK, jos1M, Janes-tag, goo300k, see the Section on PoS tagger training for details) and/or on the inflectional lexicon Sloleks.

For training your own lemmatiser for standard and non-standard Slovene, you can use the CLASSLA pipeline, which uses the external lexicon for lemmatisation (Sloleks). The documentation is available here.

Q3.5: What data is available for training a named entity recogniser for Slovene?

For training a named entity recogniser of standard language, SUK is the best resource. For training NER systems for online, non-standard texts, Janes-tag can be used. Finally, for training historical NER models, goo300k is the best resource.

The CLASSLA pipeline allows you to train your own named entity recogniser as well. The documentation is available here.

Q3.6: What data is available for training a syntactic parser for Slovene?

If you want to follow the Universal Dependencies formalism for dependency parsing, the best location for obtaining training data is the Universal Dependencies repository.

For training parsers by following the Slovene-specific formalism, the SUK dataset should be used.

You can also use the CLASSLA pipeline to train your own parser. The documentation is available here.