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  • GreynirCorrect (1.0.2)

    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/.

  • The Trankit model for linguistic processing of standard Slovenian

    This is a retrained Slovenian standard model for Trankit v1.1.1 library (https://pypi.org/project/trankit/). It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, feature prediction, and dependency parsing in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). The model was trained using a dataset published by Universal Dependencies in release 2.12 (https://github.com/UniversalDependencies/UD_Slovenian-SSJ/tree/r2.12). Due to the larger training dataset compared to the original Trankit v1.1.1 model, this version yields superior results and achieves state-of-the art parsing performance for Slovenian (https://slobench.cjvt.si/leaderboard/view/11). To utilize this model, please follow the instructions provided in our github repository (https://github.com/clarinsi/trankit-train) or refer to the Trankit documentation (https://trankit.readthedocs.io/en/latest/training.html#loading). This ZIP file contains models for both xlm-roberta-large (which delivers better performance but requires more hardware resources) and xlm-roberta-base.

  • NeMo Neural Machine Translation service RSDO-DS4-NMT-API 1.0

    Neural Machine Translation service for NeMo AAYN Base models. For more details about building such models, see the official NVIDIA NeMo documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/machine_translation/machine_translation.html) and NVIDIA NeMo GitHub (https://github.com/NVIDIA/NeMo). A model for language pair SL-EN can be downloaded from http://hdl.handle.net/11356/1736. The service accepts the source language and target language, and either a single string or list of strings to be translated. The result will be in the same format as the request, either as a single string or list of strings. The maximal accepted text length is 5000c. Note that transcription of one 5000c text block on cpu will take advantage of all available cores, consume up to 3GB RAM and may take ~200s (on a system with 24 vCPU). See the service README.md for further details.

  • Q-CAT Corpus Annotation Tool 1.0

    The Q-CAT (Querying-Supported Corpus Annotation Tool) is a computational tool for manual annotation of language 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

  • SloBENCH evaluation framework

    The evaluation framework contains public evaluation scripts. All the scripts contain additional Dockerfiles that allow for platform-independent evaluation and exact comparison of results. Pre-built Docker images are available in slobench/eval DockerHub repository. The evaluation framework is used and maintained by the SloBENCH leaderboard Web site team. SloBENCH submitters are able to check their compliance of submissions and evaluate theri model on training/validation data prior to submission. The initial version of SloBENCH contains evaluation scripts with examples of training and testing datasets for nine different tasks: named entity recognition, part-of-speech tagging, lemmatization, dependency parsing, semantic role labeling, translation (ENG-SLO, SLO-ENG), summarization and question answering.

  • NameTag 2

    NameTag 2 is a named entity recognition tool. It recognizes named entities (e.g., names, locations, etc.) and can recognize both flat and embedded (nested) entities. NameTag 2 can be used either as a commandline tool or by requesting the NameTag webservice. NameTag webservice can be found at: https://lindat.mff.cuni.cz/services/nametag/ NameTag commandline tool can be downloaded from NameTag GitHub repository, branch nametag2: git clone https://github.com/ufal/nametag -b nametag2 Latest models and documentation can be found at: https://ufal.mff.cuni.cz/nametag/2 This software subject to the terms of the Mozilla Public License, v. 2.0 (http://mozilla.org/MPL/2.0/). The associated models are distributed under CC BY-NC-SA license. Please cite as: Jana Straková, Milan Straka, Jan Hajič (2019): Neural Architectures for Nested NER through Linearization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5326-5331, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-950737-48-2 (https://aclweb.org/anthology/papers/P/P19/P19-1527/)

  • TTS Text Processing (22.10)

    ENGLISH: This project provides a TTS textprocessing pipeline for Icelandic. The pipeline includes modules for html parsing, text cleaning, text normalization for TTS, spell and grammar correction, phrasing, and grapheme-to-phoneme (g2p) conversion. Before a text can be fed into a TTS system it has to be converted into the format that was used when training that system. The format can be grapheme-based (i.e. alphabetic characters of the language in question are used as input) or phoneme-based (i.e. a phonetic alphabet like IPA or SAMPA are used as input). The TTS Textprocessing Pipeline for Icelandic offers both possibilities. ÍSLENSKA: Þessi hugbúnaðarpakki inniheldur textavinnslupípu fyrir íslenska talgervla. Pípan samanstendur af vinnslu html-skjala fyrir hljóðbækur, hreinsun texta, textanormun, stafsetningarleiðréttingu, innsetningu á þögnum og sjálfvirkri hljóðritun. Áður en hægt er að senda texta á talgervil þarf að forvinna hann, t.d. skrifa út tölustafi og skammstafanir, merkja inn þagnir og koma textanum að lokum á sama form og þjálfunargögn þess talgervils sem á að lesa textann. Yfirleitt eru talgervlar þjálfaðir á hljóðrituðum textum, þar sem textarnir eru hljóðritaðir skv. hljóðritunarstafrófum eins og IPA eða SAMPA, en einnig geta þeir verið þjálfaðir beint á textum skrifuðum með hefðbundnum bókstöfum. Textavinnslupípan býður upp á báða möguleika og einnig að vinna textann einungis að hluta.

  • Icelandic TTS for Android (22.10)

    ENGLISH: The Símarómur application provides an Icelandic TTS application for the Android TTS service. The application provides access to voices over network of the Tiro TTS API and on-device voices that are bundled via assets. The app offers connections to most of the voices that have been developed within the LT program at this time. The voices themselves and the TTS service were developed at Reykjavik University and at Tiro ehf. (see e.g. http://hdl.handle.net/20.500.12537/268) ÍSLENSKA: Símarómur er Android app sem gerir notendum kleift að nota íslenskan talgervil í símunum, t.d. sem skjálesara. Símarómur býður upp á tengingar við flestar þær raddir sem þróaðar hafa verið innan Máltækniáætlunarinnar, annars vegar gegnum vefþjónustu Tiro og hins vegar sem raddir sem keyra á símanum sjálfum. Raddirnar sem Símarómur notar voru þjálfaðar hjá Háskólanum í Reykjavík, Tiro ehf. þróaði TTS-vefþjónustuna sem Símarómur notar (sjá http://hdl.handle.net/20.500.12537/268)