Introduction to the Special Issue on the Web as Corpus

Kilgarriff, Adam; Grefenstette, Gregory

doi:10.1162/089120103322711569

Cited by 532 publications

(252 citation statements)

References 9 publications

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“…The world wide web, with its inexhaustible amount of natural language data, has become an established source for efficiently building large corpora (Kilgarriff and Grefenstette, 2003). Tools are available that make it convenient to bootstrap corpora from the web based on mere seed term lists, such as the BootCaT toolkit (Baroni and Bernardini, 2004).…”

Section: Related Workmentioning

confidence: 99%

The PAISÀ Corpus of Italian Web Texts

Lyding¹,

Stemle²,

Borghetti³

et al. 2014

Proceedings of the 9th Web as Corpus Workshop (WaC-9)

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Section: Related Workmentioning

confidence: 99%

The PAISÀ Corpus of Italian Web Texts

Lyding¹,

Stemle²,

Borghetti³

et al. 2014

Proceedings of the 9th Web as Corpus Workshop (WaC-9)

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“…However, one of the biggest problems encountered by these approaches is to obtain an amount of data that could be large enough for statistical and linguistic analysis. Taking into account the rapid growth of the Internet and the quantity of texts included in it, some researchers have proposed using the Web as a source for building corpora [7]. Two strategies have been proposed for exploiting the web with that objective in mind:…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Sense Distribution of Concordances Obtained by Web as Corpus Approach

Saralegi¹,

Gamallo

2013

Computational Linguistics and Intelligent Text Processing

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Abstract. In corpus-based lexicography and natural language processing fields some authors have proposed using the Internet as a source of corpora for obtaining concordances of words. Most techniques implemented with this method are based on information retrieval-oriented web searchers. However, rankings of concordances obtained by these search engines are not built according to linguistic criteria but to topic similarity or navigational oriented criteria, such as page-rank. It follows that examples or concordances could not be linguistically representative, and so, linguistic knowledge mined by these methods might not be very useful. This work analyzes the linguistic representativeness of concordances obtained by different relevance criteria based web search engines (web, blog and news search engines). The analysis consists of comparing web concordances and SemCor (the reference) with regard to the distribution of word senses. Results showed that sense distributions in concordances obtained by web search engines are, in general, quite different from those obtained from the reference corpus. Among the search engines, those that were found to be the most similar to the reference were the informational oriented engines (news and blog search engines).

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“…This paper extends [1] by intensive experimentation with Google as a resource for testing Spanish collocations (for English, the results would be even much more statistically significant). The Web is widely considered now as a huge (but noisy) linguistic resource [3,4]. To use it for malapropism detection and correction, we had to revise the earlier algorithm and to develop new threshold-based procedures.…”

Section: Introductionmentioning

confidence: 99%

Detection and Correction of Malapropisms in Spanish by Means of Internet Search

Bolshakov

Galicia-Haro

Gelbukh

2005

Text, Speech and Dialogue

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Abstract. Malapropisms are real-word errors that lead to syntactically correct but semantically implausible text. We report an experiment on detection and correction of Spanish malapropisms. Malapropos words semantically destroy collocations (syntactically connected word pairs) they are in. Thus we detect possible malapropisms as words that do not form semantically plausible collocations with neighboring words. As correction candidates, we select words similar to the suspected one but forming plausible collocations with neighboring words. To judge semantic plausibility of a collocation, we use Google statistics of occurrences of the word combination and of the two words taken apart. Since collocation components can be separated by other words in a sentence, Google statistics is gathered for the most probable distance between them. The statistics is recalculated to a specially defined Semantic Compatibility Index (SCI). Heuristic rules are proposed to signal malapropisms when SCI values are lower than a predetermined threshold and to retain a few highly SCIranked correction candidates. Our experiments gave promising results.

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Introduction to the Special Issue on the Web as Corpus

Abstract: The Web, teeming as it is with language data, of all manner of varieties and languages, in vast quantity and freely available, is a fabulous linguists' playground. This special issue of Computational Linguistics explores ways in which this dream is being explored.

Cited by 532 publications

References 9 publications

The PAISÀ Corpus of Italian Web Texts

The PAISÀ Corpus of Italian Web Texts

Analyzing the Sense Distribution of Concordances Obtained by Web as Corpus Approach

Detection and Correction of Malapropisms in Spanish by Means of Internet Search

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