FAQ for Croatian 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 helpdesk.classla@clarin.si, Subject “FAQ_Croatian”.

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

1. Online Croatian language resources

Q1.1: Where can I find Croatian dictionaries?

Below we list the main lexical resources:

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

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

• NoSketch Engine, an open-source variant of the well-known Sketch Engine. No registration is necessary or possible, which has some drawbacks, e.g., not being able to save your screen settings or make private subcorpora.
• 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.

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 Croatian 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. For researchers in the EU, access to SketchEngine is free for non-commercial purposes in 2018-2022.

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

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

• general language corpora are the web corpus hrWaC (1.4 billion tokens), the largest corpus of Croatian, the Riznica Croatian Language Corpus (100 million tokens) of the Institute for Croatian language and linguistics, which consists of literary works and newspaper texts and which you can query via its specialized interface as well, and The Croatian National Corpus (HNK) of the Institute of Linguistics
• specialized corpora include the parliamentary corpus ParlaMint-HR, the Wikipedia corpus for Croatian, CLASSLAWiki-hr, and Serbo-Croatian, CLASSLAWiki-sh, the corpus of news portals ENGRI, and the corpus of tweets Tweet-hr
• manually annotated corpora include the training corpus of standard language hr500k, the corpora of non-professional written language Raput-cln (speakers with language disorders) and Raput-ncln (typical speakers), and the training corpus of computer-mediated communication ReLDI-hr with manually normalised (standardised), morphosyntactically tagged and lemmatised words and named entities
• parallel corpora include the multilingual DGT translation memory corpus EU DGT-UD: Croatian

In addition to these, the CroLTeC corpus, a learner corpus of Croatian, can be queried via the TeiTok interface, and the Croatian Spoken Language Corpus is available through TalkBank. The latter platform also offers access to a small language development corpus of three participants, the Kovačević corpus, the Croatian part of the comparable corpora CHILDES, which consists of transcripts of child language for 24 languages.

Furthermore, the Sketch Engine, which is free for non-commercial purposes in 2018-2022, includes the following Croatian corpora: EUR-Lex Croatian 2/2016 and OPUS2 Croatian, which is a part of the parallel corpus of 40 languages.

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

Most of these corpora are annotated according to the MULTEXT-East morphosyntactic specifications. The more recent ones use the Version 6 specifications for the Serbo-Croatian macrolanguage. More recent corpora also use the Universal Dependencies project annotation scheme, in particular that for Croatian and Serbian. Named entities are annotated via the Janes NE guidelines.

Q1.5: Where can I download Croatian resources?

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

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

• manually annotated corpora and datasets, including the Sentiment Annotated Dataset of Croatian News, and the offensive language dataset FRENK, annotated for different types of socially unacceptable discourse
• other corpora and datasets, including the linguistically annotated corpus of parliamentary debates ParlaMint.ana, the 24sata news article archive and news comment dataset, the LiLaH emotion lexicon, the Twitter corpus, automatically constructed multiword lexicon hrMWELex, a text collection for training the BERTić transformer model BERTić-data, the Keyword extraction dataset, and the Croatian-English parallel corpora hrenWaC and the Tourism Corpus

Another point where you can find Croatian resources is the MetaShare repository, which includes the sentiment lexicon CroSentilex, the valency lexicon CROVALLEX, and the South-East European Parallel Corpus SETimes Corpus.

2. Tools to annotate Croatian texts

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

The state-of-the-art CLASSLA pipeline provides processing of standard and non-standard (Internet) Croatian on the levels of tokenisation and sentence splitting, part-of-speech tagging, lemmatisation, dependency parsing, and named entity recognition. For Croatian, the CLASSLA pipeline uses the rule-based reldi-tokeniser. There are also available off-the-shelf models for lemmatisation of standard and non-standard Croatian, and part-of-speech tagging of standard and non-standard Croatian.

The documentation for the installation and use of the pipeline is available 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 tokeniser, and part-of-speech tagger and lemmatiser.

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

• Currently, the only on-line text normalisation tool available through the CLARIN.SI services (ReLDIanno) is the REDI diacritic restorer. Its usage is documented here. You can also download it, install it and use it locally.
• For word-level normalisation of user-generated Croatian 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 Croatian. 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 Croatian texts, following the Universal Dependencies formalism, in multiple ways:

• by using the state-of-the-art CLASSLA pipeline, which also offers an off-the-shelf model
• by using the CLARIN.SI service ReLDIanno
• by using the UDPipe tool, which has off-the-shelf models for many languages, Croatian included

3. Datasets to train Croatian annotation tools

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

• The embeddings trained on the largest collection of Croatian textual data (hrWaC, Riznica, 24sata newspaper texts and comments, etc.) is the CLARIN.SI-embed.hr embedding collection.
• There are also collections of trained embeddings for Croatian available from fastText.
• If you want to train your own embeddings, the largest freely available collection of Croatian texts is the BERTić-data text collection.

You can also use a transformer language model BERTić, 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.

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

For training text normalisers for Internet Croatian, the ReLDI-NormTagNER-hr dataset can be used, a gold-standard training and testing dataset for tokenisation, sentence segmentation, word normalisation, morphosyntactic tagging, lemmatisation and named entity recognition of non-standard Croatian.

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

The reference dataset for training a standard tagger is hr500k. There is also the ReLDI-NormTagNER-hr training dataset of Internet Croatian.

You can also use the CLASSLA pipeline in combination with the CLARIN.SI embeddings and the training dataset hr500k 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 Croatian?

Lemmatisers can be trained either on the tagger training data (hr500k, ReLDI-NormTagNER-hr, see the section on PoS tagger training for details) and/or on the inflectional lexicon hrLex.

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

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

For training a named entity recogniser of standard language, hr500k is the best resource. For training NER systems for online, non-standard texts, ReLDI-NormTagNER-hr can be used.

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 Croatian?

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

If you require additional annotation layers, e.g., for multi-task learning, the hr500k dataset should be used.

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