Challenge: The CURLICAT CEF Telecom project aims to collect and deeply annotate a set of large corpora from selected domains.
Approach: They present the results of the CURLICAT CEF Telecom project . they propose to collect and deeply annotate a set of large corpora from selected domains .
Outcome: The CURLICAT CEF Telecom project provides a set of large corpora from selected domains . the corporatized corporates are tokenized, lemmatized and morphologically analysed .

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The MARCELL Legislative Corpus (2020.lrec-1)

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Challenge: MARCELL corpus provides a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.
Approach: They present the results of the project MARCELL CEF Telecom . they aim to collect and deeply annotate a large comparable corpus of legal documents .
Outcome: The MARCELL corpus includes 7 monolingual sub-corpora containing the body of respective national legislative documents.
An Empirical Evaluation of Annotation Practices in Corpora from Language Documentation (2020.lrec-1)

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Challenge: Language documentation projects have produced substantial amounts of primary data from a wide variety of endangered languages.
Approach: They propose to use common annotation conventions in existing corpora to facilitate their future processing.
Outcome: The proposed formats are based on the common ELAN and Toolbox formats and are used to facilitate their future processing.
CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation (2024.lrec-main)

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Challenge: Using a similar crawling setup, the corpora are comparable across the entire South Slavic language space.
Approach: They propose to collect 13 billion tokens of texts from 26 million documents . they are linguistically annotated with a CLASSLA-Stanza pipeline and enriched with document-level genre information via a Transformer-based multilingual classifier.
Outcome: The corpora are linguistically annotated with the state-of-the-art CLASSLA-Stanza linguistic processing pipeline and enriched with document-level genre information via the Transformer-based multilingual X-GENRE classifier.
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.
Outcome: The proposed corpus is searchable through a couple of well-established corpus infrastructures.
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)

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Challenge: Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding.
Approach: They propose to use sense-annotated corpora for supervised Word Sense Disambiguation.
Outcome: The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available.
Do Language Models Care about Text Quality? Evaluating Web-Crawled Corpora across 11 Languages (2024.lrec-main)

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Challenge: Large, curated, web-crawled corpora play a vital role in training language models . however, relatively little attention has been given to the quality of these corporata .
Approach: They compare four of the currently most relevant large, web-crawled corpora across eleven lower-resourced European languages to evaluate their quality.
Outcome: The CC100 corpus achieves the highest scores on the tests in 11 lower-resourced European languages.
Corpus Services: A Framework to Curate XML Corpus Data (2024.lrec-main)

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Challenge: Existing corpora on Samoyedic ( Uralic) languages include the INEL Kamas Corpus and the INL Selkup Corpus .
Approach: They describe the Corpus Services framework, a collection of Java validation tools for language corpora compiled in XML-based data formats.
Outcome: The proposed framework is integrated into the curation and publication workflows for EXMARaLDA-driven corpora of Northern Eurasian languages, as developed by the long-term project INEL .
A Repository of Corpora for Summarization (L18-1)

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Challenge: Summarization corpora are numerous but fragmented, making it difficult to pinpoint corporata best suited for a given summarization task.
Approach: They propose a repository containing corpora available to train and evaluate automatic summarization systems.
Outcome: The proposed system is based on a repository of corpora available for summarization tasks.
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.
Outcome: The proposed dataset shows that blocklist filtering removes text from minority individuals and patents.
Creation of a Balanced State-of-the-Art Multilayer Corpus for NLU (L18-1)

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Challenge: Using full stack of language resources, we are creating a balanced text corpus for Latvian.
Approach: They propose to create a syntactically and semantically annotated multilayered corpus for Latvian . they use widely acknowledged and cross-lingual representations for the corpus .
Outcome: The proposed corpus adopts widely recognized and cross-lingual representations for natural language understanding and generation in Latvian.

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