Introduction to the Special Issue on the Web as Corpus

By Adam Kilgarriff, Gregory Grefenstette. International Conference on Computational Logic, 2003

761 citations recorded by the source corpus.

Subfields: Information Systems, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/e965483087d63b9ca957fa04c6e639d945b3a49b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Human-Computer Interaction

Citations in the corpus

BootCaT: Bootstrapping Corpora and Terms from the Web (2004, background): “), there is no doubt that the World Wide Web is a mine of language data of unprecedented richness and ease of access (Kilgarriff and Grefenstette, 2003).”
Pattern for Python (2012, background): “The World Wide Web is an immense collection of linguistic information that has in the last decade gathered attention as a valuable resource for tasks such as machine translation, opinion mining and trend detection, that is, " Web as Corpus " (Kilgarriff and Grefenstette, 2003).”
Biases in Predicting the Human Language Model (2014, background): “This furthers the arguments of the “the web as corpus” community (Kilgarriff and Grefenstette, 2003) with respect to psycholinguistics.”
Using the Structure of a Conceptual Network in Computing Semantic Relatedness (2005, background): “[ 18 ] provide estimates of the Web size in words, as indexed by Altavista, for the German language: 7,035,850,000.”
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, background): “which resist standardization are reduced or excluded cited by [29]. Natural language processing’s historical use of carefully constructed balanced datasets—prior to the proliferation of internet data [79]—points to lessons about dataset design which remain relevant. These include the necessity of de”
What helps where – and why? Semantic relatedness for knowledge transfer (2010, background): “With respect to SR, various measures have been proposed that use hit counts to measure term co-occurrence information as an indicator of term relatedness [17].”
Big Data: Deep Learning for financial sentiment analysis (2018, methodology): “Long shortterm memory (LSTM) and Recurrent Neural Networks are two examples of neural networks that can increase the accuracy of prediction by picking up on activity hundreds of time steps in the past.”
Introducing, evaluating ukWaC, a very large web-derived corpus of English (2008, neutral): “While the latter is a legitimate object for ‘web linguistics’ (Kilgarriff and Grefenstette, 2003), its pursuit is not among the priorities set out for the WaCky corpora.”
Software Framework for Topic Modelling with Large Corpora (2010, methodology): “We want the package to be able to detect topics based on corpora which are larger than the available RAM, in accordance with the current trends in NLP (see e.g. (Kilgarriff and Grefenstette, 2003)).”
Metaphor Aptness and Conventionality: A Processing Fluency Account (2011, methodology): “While we can gain a general sense of frequency from using the Google search engine, it should be noted that there are drawbacks to using Google as a corpus (eg, Kilgarriff & Grefenstette, 2003 ).”
The WaCky wide web: a collection of very large linguistically processed web-crawled corpora (2009, background): “While the latter is a legitimate object for 'web linguistics' (Kilgarriff and Grefenstette 2003), its pursuit is not among the priorities set out for the WaCky corpora.”
Biases in Large Language Models: Origins, Inventory, and Discussion (2023, background): “Although modern language models are trained on massive corpora [45, 78, 82, 107, 108], the documents that make up their training dataset are still a subset of the text available on the Web [27, 51, 132].”
Corpus Use in Language Learning: A Meta-Analysis. (2017, background): “Learners are already involved in using information and communication technology (ICT) to search for answers to their language questions, especially via the use of Google as a “concordancer” for the web as “corpus” (Chinnery, 2008; Kilgarriff & Grefenstette, 2003).”