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  • Upload2S3 (22.06)

    [ENGLISH] This project is a simple and straight forward way to upload podcast data like text files via a form to an AWS S3 bucket. This web application codebase is minimally derived from an existing ReactJS web application, samromur-chat. However, using this codebase does not require knowledge of samromur-chat. [ÍSLENSKA] Þetta verkefni býður upp á einfalda leið til þess að hlaða upp hlaðvarpsgögnum, t.d. textagögnum, á AWS S3. Kóðinn er lítillega byggður á ReactJS-forritinu samromur-chat. Ekki þarf þó þekkingu á samromur-chat til þess að nota þetta tól.

  • WordnetLoom 1.68.2

    WordnetLoom – is an wordnet editor application built for the needs of the construction of a the largest Polish wordnet called plWordNet. WordnetLoom provides two means of interaction: a form-based, implemented initially, and a visual, graph-based introduced recently. The visual, graph-based interactive presentation of the wordnet structure enables browsing and its direct editing on the structure of lexico-semantic relations and synsets. WordnetLooms works in a distributed environment, i.e. several linguists can work simulanuously from different sites on the same central database.

  • GreynirPackage 3.5.2 (22.10)

    GreynirPackage is a Python 3 package for working with Icelandic natural language text. Greynir can parse text into sentence trees, find lemmas, inflect noun phrases, assign part-of-speech tags and much more. Greynir's sentence trees can inter alia be used to extract information from text, for instance about people, titles, entities, facts, actions and opinions. Greynir uses the Tokenizer package, by the same authors, to tokenize text (see http://hdl.handle.net/20.500.12537/262). More information at https://github.com/icelandic-lt/GreynirEngine and detailed documentation at https://greynir.is/doc/. GreynirPackage er Python 3 pakki sem vinnur með íslenskan texta. Greynir þáttar texta í setningar, lemmar og markar texta, beygir nafnliði og margt fleira. Hægt er að nýta þáttunartrén sem tólið býr til í þeim tilgangi að draga upplýsingar út úr texta, til dæmis um manneskjur, starfstitla, sérnafnaeiningar, staðreyndir, atburði og skoðanir. Greynir notar Tokenizer-pakkann, eftir sömu höfunda, til að tilreiða texta (sjá http://hdl.handle.net/20.500.12537/262). Frekari upplýsingar má finna á https://github.com/icelandic-lt/GreynirEngine og ítarlega skjölun (á ensku) á https://greynir.is/doc/.

  • Tokenizer for Icelandic text (3.3.2)

    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

  • OptaHopper: phrase-level sentiment with opinion targets

    A phrase- and sentence-level sentiment analysis tool (deep-learning TreeLSTM, TreeHopper) integrated with opinion finding. Any sentiment dictionary may be used as an input feature, including lemma-level and plWordNet emo dictionaries. In the case of plWordNet emo, provided integration with the WSD module. The OPFI (Opinion Finder) app used for opinion target extraction.

  • ABLTagger (Lemmatizer) - 3.1.0

    A neural Lemmatizer for Icelandic. In this submission, you will find a pretrained lemmatizer model for ABLTagger v3.1.0. In this submission we provide a small lemmatizer that accepts as input the tokens and tags from the revised tagset. The lemmatizer achieves an accuracy of 98.3% on MIM-Gold (21.05, cross-validation). Það er minni nákvæmni en Nefnir. For installation, usage, and other instructions see https://github.com/icelandic-lt/POS. You should also check if a newer version is out (see README.md - versions) on CLARIN: - Model files ------------------------------------------------------------------------------------------- Lemmari fyrir íslensku. Í þessum pakka er forþjálfað lemmunar líkan fyrir ABLTagger v3.1.0. Í þessari útgáfu er lítill lemmari sem tekur inn tóka og mörk úr nýja markamengið. Lemmarinn nær 98.3% nákvæmni á MÍM-Gull (21.05, krossprófanir). Það er minni nákvæmni en Nefnir. Fyrir uppsetningar-, notenda- og aðrar leiðbeiningar sjá https://github.com/icelandic-lt/POS. Einnig er gott að athuga þar hvort ný útgáfa sé komin út (sjá README.md - versions) Á CLARIN: - Gögn fyrir líkan

  • ABLTagger (PoS) - 1.0.0

    A Part-of-Speech (PoS) tagger for Icelandic. In this submission, you will find ABLTagger v1.0.0. This is a PoS tagger that works with the revised tagset and achieves an accuracy of 95.59% on MIM-Gold (cross-validation). For additional details, error analysis and categorization of this tagger and other taggers (including a previous version of ABLTagger), see I4 report for milestone (2020) in Language Technology Programme for Icelandic 2019-2023. For the most recent versions, installation, usage, and other instructions see https://github.com/cadia-lvl/POS on CLARIN: - Python wheel, version 1.0.0 - GitHub repository at version 1.0.0 - Model files (tagger and dictionaries) - Docker image, version 1.0.0 ------------------------------------------------------------------------------------------- Markari fyrir íslensku. Í þessum pakka er ABLTagger v.1.0.0. Þetta er markari sem virkar fyrir nýja markamengið og nær 95.59% nákvæmni á MÍM-Gull (krossprófanir). Fyrir nánari upplýsingar, villugreiningu og villuflokkun fyrir þennan markara og aðra (ásamt fyrri útgáfu af þessum markara), sjá I4 skýrslu fyrir vörðu 3 (2020) í Máltækniáætlun fyrir íslensku 2019-2023. Fyrir nýjustu útgáfur, uppsetninga-, notenda- og aðrar leiðbeiningar sjá https://github.com/cadia-lvl/POS Á CLARIN: - Python wheel, útgáfa 1.0.0 - GitHub repository af útgáfu 1.0.0 - Líkan (markari and orðabækur) - Docker mynd, útgáfa 1.0.0

  • 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.

  • The Orange workflow for observing collocation clusters ColEmbed 1.0

    The Orange Workflow for Observing Collocation Clusters ColEmbed 1.0 ColEmbed is a workflow (.OWS file) for Orange Data Mining (an open-source machine learning and data visualization software: https://orangedatamining.com/) that allows the user to observe clusters of collocation candidates extracted from corpora. The workflow consists of a series of data filters, embedding processors, and visualizers. As input, the workflow takes a tab-separated file (.TSV/.TAB) with data on collocations extracted from a corpus, along with their relative frequencies by year of publication and other optional values (such as information on temporal trends). The workflow allows the user to select the features which are then used in the workflow to cluster collocation candidates, along with the embeddings generated based on the selected lemmas (either one lemma or both lemmas can be selected, depending on our clustering criteria; for instance, if we wish to cluster adjective+noun candidates based on the similarities of their noun components, we only select the second lemma to be taken into account in embedding generation). The obtained embedding clusters can be visualized and further processed (e.g. by finding the closest neighbors of a reference collocation). The workflow is described in more detail in the accompanying README file. The entry also contains three .TAB files that can be used to test the workflow. The files contain collocation candidates (along with their relative frequencies per year of publication and four measures describing their temporal trends; see http://hdl.handle.net/11356/1424 for more details) extracted from the Gigafida 2.0 Corpus of Written Slovene (https://viri.cjvt.si/gigafida/) with three different syntactic structures (as defined in http://hdl.handle.net/11356/1415): 1) p0-s0 (adjective + noun, e.g. rezervni sklad), 2) s0-s2 (noun + noun in the genitive case, e.g. ukinitev lastnine), and 3) gg-s4 (verb + noun in the accusative case, e.g. pripraviti besedilo). It should be noted that only collocation candidates with absolute frequency of 15 and above were extracted. Please note that the ColEmbed workflow requires the installation of the Text Mining add-on for Orange. For installation instructions as well as a more detailed description of the different phases of the workflow and the measures used to observe the collocation trends, please consult the README file.