2006 IntroToTheSpecIssOnTheWebAsCorpus

(Kilgarriff & Grefenstette, 2006) ⇒ Adam Kilgarriff, and Gregory Grefenstette. (2006). “Introduction to the Special Issue on the Web as Corpus.” In: Computational Linguistics, 29(3). doi:10.1162/089120103322711569

Subject Headings: Web Corpus, Corpus.

Notes

Cited By

Quotes

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.

1. Introduction

The Web is immense, free, and available by mouse click. It contains hundreds of billions of words of text and can be used for all manner of language research. The simplest language use is spell checking. Is it speculater or speculator? Google gives 67 for the former (usefully suggesting the latter might have been intended) and 82,000 for the latter. Question answered.

Language scientists and technologists are increasingly turning to the Web as a source of language data, because it is so big, because it is the only available source for the type of language in which they are interested, or simply because it is free and instantly available. The mode of work has increased dramatically from a standing start seven years ago with the Web being used as a data source in a wide range of research activities: The papers in this special issue form a sample of the best of it. This introduction to the issue aims to survey the activities and explore recurring themes. We first consider whether the Web is indeed a corpus, then present a history of the theme in which we view the Web as a development of the empiricist turn that has brought corpora center stage in the course of the 1990s. We briefly survey the range of Web-based NLP research, then present estimates of the size of the Web, for English and for other languages, and a simple method for translating phrases. Next we open the Pandora’s box of representativeness (concluding that the Web is not representative of anything other than itself, but then neither are other corpora, and that more work needs to be done on text types). We then introduce the articles in the special issue and conclude with some thoughts on how the Web could be put at the linguist’s disposal rather more usefully than current search engines allow.

1.1 Is the Web a Corpus?

To establish whether the Web is a corpus we need to find out, discover, or decide what a corpus is. McEnery and Wilson (1996, page 21) say

In principle, any collection of more than one text can be called a corpus. . . But the term “corpus” when used in the context of modern linguistics tends most frequently to have more specific connotations than this simple definition provides for. These may be considered under four main headings: sampling and representativeness, finite size, machine-readable form, a standard reference.

We would like to reclaim the term from the connotations. Many of the collections of texts that people use and refer to as their corpus, in a given linguistic, literary, or language-technology study, do not fit. A corpus comprising the complete published works of Jane Austen is not a sample, nor is it representative of anything else. Closer to home, Manning and Schutze (1999, page 120) observe:

In Statistical NLP, one commonly receives as a corpus a certain amount of data from a certain domain of interest, without having any say in how it is constructed. In such cases, having more training data is normally more useful than any concerns of balance, and one should simply use all the text that is available.

We wish to avoid a smuggling of values into the criterion for corpus-hood. McEnery and Wilson (following others before them) mix the question “What is a corpus?” with “What is a good corpus (for certain kinds of linguistic study)?” muddying the simple question “Is corpus x good for task y?” with the semantic question “Is x a corpus at all?” The semantic question then becomes a distraction, all too likely to absorb energies that would otherwise be addressed to the practical one. So that the semantic question may be set aside, the definition of corpus should be broad. We define a corpus simply as “a collection of texts.” If that seems too broad, the one qualification we allow relates to the domains and contexts in which the word is used rather than its denotation: A corpus is a collection of texts when considered as an object of language or literary study.

The answer to the question “Is the web a corpus?” is yes.

2. History

2.2 The 100M Words of the BNc

…

Another argument is made vividly by Banko and Brill (2001). They explore the performance of a number of machine learning algorithms (on a representative disambiguation task) as the size of the training corpus grows from a million to a billion words. All the algorithms steadily improve in performance, though the question “Which is best?” gets different answers for different data sizes. The moral: Performance improves with data size, and getting more data will make more difference than fine-tuning algorithms.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 IntroToTheSpecIssOnTheWebAsCorpus	Adam Kilgarriff Gregory Grefenstette			Introduction to the Special Issue on the Web as Corpus		Computational Linguistics (CL) Research Area	http://ucrel.lancs.ac.uk/acl/J/J03/J03-3001.pdf	10.1162/089120103322711569		2006