| Challenge: | Unlike previous Wiktionary extractions, the new extractor, Wiktextract, fully interprets and expands templates and Lua modules in Wiktionaries. |
| Approach: | They propose a machine-readable structured version of Wiktionary that interprets and expands templates and Lua modules. |
| Outcome: | The extracted data is multilingual and includes lemmas, inflected forms, translations, etymology, usage examples, pronunciations, and various morphological, syntactic, semantic, topical, and dialectal annotations. |
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| Challenge: | ENGLAWI is a structured and normalized version of the English Wiktionary encoded into a workable XML format. |
| Approach: | They introduce ENGLAWI, a large, versatile, XML-encoded machine-readable dictionary extracted from Wiktionary. |
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Wiktionary Normalization of Translations and Morphological Information (2020.coling-main)
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| Challenge: | We extend the Yawipa Wiktionary Parser to extract and normalize translations from etymology glosses and morphological form-of relations. |
| Approach: | They extend Yawipa to extract and normalize translations from etymology glosses . they propose a method to identify typos in translation annotations based on extracted morphological data . |
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Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus (2020.lrec-1)
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| Challenge: | Inflectional corpora with annotated morpheme boundaries are scarce in the NLP community . a generated, multilingual inflectional lexicon with morphological features is not as good as UniMorph's . |
| Approach: | They evaluate a multilingual inflectional corpus with morpheme boundaries from the English Wiktionary and the UniMorph project's inflection corpus. |
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Using Wiktionary to Create Specialized Lexical Resources and Datasets (2022.lrec-1)
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| Challenge: | Using Wiktionary data to build specialized lexical datasets can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Machine Translation (MT). |
| Approach: | They propose to use Wiktionary data to create specialized lexical datasets that can be used for evaluating or improving NLP tasks. |
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Massively Multilingual Pronunciation Modeling with WikiPron (2020.lrec-1)
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Jackson L. Lee, Lucas F.E. Ashby, M. Elizabeth Garza, Yeonju Lee-Sikka, Sean Miller, Alan Wong, Arya D. McCarthy, Kyle Gorman
| Challenge: | WikiPron is an open-source command-line tool for extracting pronunciation data from Wiktionary . the tool generates a database of 1.7 million pronunciations from 165 languages . |
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Knowledge Extraction From Texts Based on Wikidata (2022.naacl-industry)
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| Challenge: | Existing knowledge extraction pipelines for English are not suitable for enterprise use. |
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Mathieu Sibue, Andrés Muñoz Garza, Samuel Mensah, Pranav Shetty, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
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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. |
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Computational Etymology and Word Emergence (2020.lrec-1)
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| Challenge: | etymology is the study of words' origins. |
| Approach: | They develop an extensible Wiktionary parser that predicts the etymology of a word across the full range of ethymological types and languages in Wiktionaries. |
| Outcome: | The proposed parser predicts the etymology of a word across the full range of ethymologies and languages in Wiktionary, and shows the application of tymatics in modeling this phenomenon. |
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
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