Risamálheild: A Very Large Icelandic Text Corpus (L18-1)

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Challenge: The corpus contains more than one billion running words from mostly contemporary texts.
Approach: They present the Icelandic Gigaword Corpus (IGC) with minimal work and resources.
Outcome: The Icelandic Gigaword Corpus (IGC) contains more than one billion running words from mostly contemporary texts.

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Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus (2022.lrec-1)

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Challenge: The Icelandic Gigaword Corpus was first published in 2018 and has since grown to include more than 50 million words.
Approach: They describe the evolution of the Icelandic Gigaword Corpus in its first four years . they show how the corpus has grown almost 50% in size from the first version to the fourth .
Outcome: The Gigaword corpus has grown 50% from its first version to its fourth version and is now available under permissive licenses.
Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users (2020.lrec-1)

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Challenge: Gigaword corpus is a large text corpus used in natural language processing . large corpora are needed to achieve better performance in the field of NLP .
Approach: They propose a set of tools to facilitate the use of the Icelandic Gigaword Corpus . they provide n-grams based on the corpus, and a variety of pre-trained word embeddings models .
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A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models (2022.lrec-1)

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Challenge: Pre-trained neural language models have shown impressive results when adapted for a variety of classification and text generation tasks.
Approach: They propose to use Icelandic's Icelandic Common Crawl Corpus to train language models that achieve state-of-the-art performance in downstream tasks.
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IceSum: An Icelandic Text Summarization Corpus (2021.naacl-srw)

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Challenge: Automatic Text Summarization (ATS) is the task of generating concise and fluent summaries from one or more documents.
Approach: They present a corpus of 1,000 Icelandic news articles and extractive summaries . they train several neural network-based models on the corpus and evaluate them .
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Samrómur: Crowd-sourcing Data Collection for Icelandic Speech Recognition (2020.lrec-1)

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Challenge: Samrómur is a web application built upon Mozilla Foundation’s open-source voice collection platform Common Voice.
Approach: They describe an ongoing project of speech data collection using the web application Samrómur which is built upon Common Voice, Mozilla Foundation’s web platform for open-source voice collection.
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The Norwegian Colossal Corpus: A Text Corpus for Training Large Norwegian Language Models (2022.lrec-1)

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Challenge: Norwegian is one of many languages lacking sufficient textual data to train quality language models.
Approach: They propose to release 49GB of clean Norwegian textual data containing over 7B words . they hope to foster the creation of better Norwegian language models and multilingual language models .
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Text Mining for History: first steps on building a large dataset (L18-1)

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Challenge: a new corpus on the history domain is being created to mine text in the domain . primary motivation for the project is the need to query the material in a non-linear way .
Approach: They propose to use a Brazilian historical-biographical dictionary as a resource for text mining.
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)

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Challenge: Until recently, language descriptions were available in paper form only, with indexes as the only search aid.
Approach: They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful.
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021.emnlp-main)

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Challenge: Large text corpora are often introduced with minimal documentation . documenting collection process, composition, intended uses, and other are key for structured, task-specific datasets.
Approach: They propose to document a dataset created by applying filters to a single snapshot of Common Crawl.
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Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies (2020.lrec-1)

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Challenge: Icelandic language is low-resource and is not yet considered in imminent danger . efforts underway to make it accessible and usable in Language Technology .
Approach: They propose to build a parallel Icelandic dependency treebank based on Universal Dependencies (UD) this is the first parallel treebank resource for the language and several other languages already have one .
Outcome: The proposed treebank is the first parallel treebank resource for the low-resource language . the project will be published as part of UD version 2.6.

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