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  • GreynirCorrect (3.2.0)

    GreynirCorrect is a Python 3 package and a command line tool for checking and correcting various types of spelling and grammar errors in Icelandic text. GreynirCorrect relies on the Tokenizer package, by the same authors, to tokenize text. More information can be found at https://github.com/mideind/GreynirCorrect, and detailed documentation at https://yfirlestur.is/doc/. GreynirCorrect er Python 3 pakki og skipanalínutól sem bendir á og leiðréttir ýmsar tegundir stafsetningar- og málvillna í íslenskum texta. GreynirCorrect reiðir sig á Tokenizer-pakkann, eftir sömu höfunda, til að tilreiða textann. Frekari upplýsingar má finna á https://github.com/mideind/GreynirCorrect, og ítarlega skjölun (á ensku) á https://yfirlestur.is/doc/.

  • Text classification model fastText-Trendi-Topics 1.0

    The fastText-Trendi-Topics model is a text classification model for categorizing news texts with one of 13 topic labels. It was trained on a set of approx. 36,000 Slovene texts from various Slovene news sources included in the Trendi Monitor Corpus of Slovene (http://hdl.handle.net/11356/1590) such as "rtvslo.si", "sta.si", "delo.si", "dnevnik.si", "vecer.com", "24ur.com", "siol.net", "gorenjskiglas.si", etc. The texts were semi-automatically categorized into 13 categories based on the sections under which they were published (i.e. URLs). The set of labels was developed in accordance with related categorization schemas used in other corpora and comprises the following topics: "črna kronika" (crime and accidents), "gospodarstvo, posel, finance" (economy, business, finance), "izobraževanje" (education), "okolje" (environment), "prosti čas" (free time), "šport" (sport), "umetnost, kultura" (art, culture), "vreme" (weather), "zabava" (entertainment), "zdravje" (health), "znanost in tehnologija" (science and technology), "politika" (politics), and "družba" (society). The categorization process is explained in more detail in Kosem et al. (2022): https://nl.ijs.si/jtdh22/pdf/JTDH2022_Kosem-et-al_Spremljevalni-korpus-Trendi.pdf The model was trained on the labeled texts using the word embeddings CLARIN.SI-embed.sl 1.0 (http://hdl.handle.net/11356/1204) and validated on a development set of 1,293 texts using the fastText library, 1000 epochs, and default values for the rest of the hyperparameters (see https://github.com/TajaKuzman/FastText-Classification-SLED for the full code). The model achieves a macro-F1-score of 0.85 on a test set of 1,295 texts (best for "vreme" at 0.97, worst for "prosti čas" at 0.67). Please note that the SloBERTa-Trendi-Topics 1.0 text classification model is also available (http://hdl.handle.net/11356/1709) that achieves higher classification accuracy, but is slower and computationally more demanding.

  • Tokenizer for Icelandic text (3.3.3)

    Tokenizer is a compact pure-Python (2.7 and 3) executable program and module for tokenizing Icelandic text. It converts input text to streams of tokens, where each token is a separate word, punctuation sign, number/amount, date, e-mail, URL/URI, etc. It also segments the token stream into sentences, considering corner cases such as abbreviations and dates in the middle of sentences. More information at: https://github.com/mideind/Tokenizer Tokenizer er pakki fyrir Python 2.7 og 3, ásamt skipanalínutóli, sem sér um tilreiðslu íslensks texta. Pakkinn umbreytir inntakstexta í tókastraum. Hver tóki er stakt orð, greinarmerki, tala/upphæð, dags-/tímasetning, netfang, vefslóð o.s.frv. Tólið skiptir tókastraumnum einnig í setningar og tekur tillit til jaðartilvika eins og skammstafana og dagsetninga í miðjum setningum. Frekari upplýsingar á: https://github.com/mideind/Tokenizer

  • Icelandic Homograph Classifier (24.04.)

    IceHoC is a binary classifier for Icelandic homographs following the pattern V-ll-(V|$) where the 'll' can be pronounced either /tl/ or /l/. The classifier was trained on the Labeled Corpus of Icelandic Homographs (http://hdl.handle.net/20.500.12537/327). Please refer to the projects README for further discussions and guidelines for usage. IceHoC er tól sem flokkar íslensk samstafa orð sem fylgja mynstrinu V-ll-(V|$), eða sérhljóð-ll-sérhljóð_eða_lok_orðs. Í þessum orðum er 'll' borið fram ýmist /tl/ eða /l/, eftir merkingu orðsins. IceHoC var þjálfað á málheild íslenskra samstafa orða (http://hdl.handle.net/20.500.12537/327). Fyrir nánari umfjöllun og leiðbeiningar um notkun, sjá README.

  • DIGIRES COVID-19 ML Dataset v.1

    DIGIRES COVID-19 ML dataset v.1 is a tab-separated (.tsv) file prepared for training machine learning algorithms. The training dataset was compiled from various internet public Lithuanian media sources. It contains 351 records and has the following attributes: "Title": the title of a news article "Text": the text of the article "Label": a label that marks the article as 1: unreliable; 0: reliable 1) "unrealiable" marks articles, which were identified by professional fact checkers as fake news; 2) "reliable" marks trustworthy articles. Classes Labels Word tokens Reliable: 175 67902 Unreliable: 176 118747 Total 351 186649

  • Yfirlestur Docs 22.10

    Yfirlestur Docs is the source code for a spelling and grammar correction add-on for Icelandic, for use with Google Docs. The plugin provides error annotation and replacement, based on user interaction. The source code is intended for third party development and can be installed and tested locally using Node.js. The plugin requires third party correction software for its functionality. For development and testing, the open-access Yfirlestur.is API produced by Miðeind was used (see:https://github.com/icelandic-lt/Yfirlestur) but is not intended for production use. This software is licensed under the MIT License. More information at https://github.com/icelandic-lt/Yfirlestur-Docs. Yfirlestur Docs er bakendakóði viðbótar fyrir Google Docs sem býður upp á leiðréttingu stafsetningar- og málfræðivillna. Viðbótin inniheldur notendaviðmót sem sýnir villur í textaskjali og býður notandanum að taka afstöðu til þeirra. Bakendakóðinn er ætlaður til utanaðkomandi þróunar og hægt er að prufukeyra viðbótina með því að ræsa vinnuumhverfi viðbótarinnar með NodeJS. Viðbótin þarf á utanaðkomandi leiðréttingarhugbúnaði að halda til að leiðrétta texta. Í þróunarferlinu var notast við forritaskilin á vegum Yfirlestur.is (sjá: https://github.com/icelandic-lt/Yfirlestur) en ekki er ætlast til að þau séu notuð í opinberri útgáfu viðbótarinnar.

  • Q-CAT Corpus Annotation Tool 1.4

    The Q-CAT (Querying-Supported Corpus Annotation Tool) is a tool for manual linguistic annotation of corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system. Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI). Version 1.4 introduces new features in command line mode (filtering by sentence ID, multiple link type visualizations)