The WaCky wide web: a collection of very large linguistically processed web-crawled corpora

By Marco Baroni, Silvia Bernardini, Adriano Ferraresi, Eros Zanchetta. 2009

1,279 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Experience Grounds Language (2020, background): “Only relatively recently (Baroni et al., 2009) has the cost of annotations decreased enough, and have large-scale webcrawls become viable, to enable the introduction of more complex text-based tasks.”
Word Translation Without Parallel Data (2017, methodology): “…of the monolingual embeddings For the word translation task, we obtained a signiﬁcant boost in performance when considering fastText embeddings trained on Wikipedia, as opposed to previously used CBOW embeddings trained on the WaCky datasets (Baroni et al. (2009)), as can been seen in Table 2.”
Word Representations via Gaussian Embedding (2014, methodology): “Unsupervised embeddings are learned on the concatenated ukWaC and WaCkypedia corpora (Baroni et al., 2009), consisting of about 3 billion tokens.”
Learning bilingual word embeddings with (almost) no bilingual data (2017, methodology): “In the case of German, the embeddings were trained on the 0.9 billion word corpus SdeWaC, which is part of the WaCky collection (Baroni et al., 2009) that was also used for English and Italian.”
Resources and benchmark corpora for hate speech detection: a systematic review (2020, methodology): “For Italian and German, we use the frequency counts for general language from the ItWaC and DeWaC corpora (Baroni et al. 2009); for English, we compute the word frequencies from the British National Corpus (Clear 1993); for Spanish we compute the word frequencies from the Spanish Billion Word…”
Probabilistic FastText for Multi-Sense Word Embeddings (2018, methodology): “We use F R W AC (French), D E W AC (German), I T W AC (Italian) datasets (Baroni et al., 2009) for text cor-puses, consisting of 1 .”
Structured Lexical Similarity via Convolution Kernels on Dependency Trees (2011, methodology): “The ﬁrst approach is LSA-based: LSA was applied to ukWak (Baroni et al., 2009), which is a large scale document collection made by 2 billion tokens.”
Convolutional Neural Network Language Models (2016, methodology): “Finally, we took a subset of the ukWaC corpus, which was constructed by crawling UK websites (Baroni et al., 2009).”
Multilingual Reliability and “Semantic” Structure of Continuous Word Spaces (2015, methodology): “Baroni et al. (2014) presented experiments where predict vectors outperform count vectors on several semantic benchmarks involving semantic relatedness, word clustering, and selectional preferences. Several open questions regarding predict vectors remain. In this paper, we focus on two shortcomings ”
Joint predictiveness in inflectional paradigms (2016, methodology): “To compensate for this, we conducted a study on the basis of FrWaC (Baroni et al., 2009), a web corpus of 1.6 billion word tokens.”
CommonCOW: Massively Huge Web Corpora from CommonCrawl Data and a Method to Distribute them Freely under Restrictive EU Copyright Laws (2016, methodology): “Other projects have either used the same or similar available software (Baroni et al., 2009) or created their own software in order to optimize the efﬁciency of the crawling process (Suchomel and Pomikálek, 2012).”
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. (2016, methodology): “Our source corpus combines the English Wikipedia snapshot from July 2015 (1.8B tokens), Araneum Anglicum Maius (1.2B) (Benko, 2014) and ukWaC (2B) (Baroni et al., 2009).”
Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD (2012, methodology): “Here, a simple stemming algorithm (Porter, 1980) was applied to the standard ukWaC corpus to give a stemmed ukWaC corpus, and that was compared with both the standard and lemmatized versions of the ukWaC corpus (Baroni et al., 2009).”
The TenTen Corpus Family (2013, background): “Baroni and Kilgarriff (2006), Sharoff (2006), Baroni et al (2009), and Kilgarriff et al (2010) present the case for web corpora and programmes in which a number of them have been developed.”
Large-scale learning of word relatedness with constraints (2012, background): “• UKWaC [1] is a corpus built by crawling of the .”
Know thy Corpus! Robust Methods for Digital Curation of Web corpora (2020, methodology): “Usually Web corpora consist of millions of Web pages retrieved as a result of making queries to the search engines (Sharoff, 2006), crawling from a seed list (Baroni et al., 2009) or taking URLs which the users shared through social media, such as reddit or Twitter (Radford et al.”