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Article published In:
The Dynamics of Lexical Innovation: Data, methods, models
Edited by Daphné Kerremans, Jelena Prokić, Quirin Würschinger and Hans-Jörg Schmid
[Pragmatics & Cognition 25:1] 2018
► pp. 174–200

Using data-mining to identify and study patterns in lexical innovation on the web

The NeoCrawler

| Ludwig-Maximilians-Universität München
| Ludwig-Maximilians-Universität München
| Ludwig-Maximilians-Universität München
| Ludwig-Maximilians-Universität München

This paper presents the NeoCrawler – a tailor-made webcrawler, which identifies and retrieves neologisms from the Internet and systematically monitors the use of detected neologisms on the web by means of weekly searches. It enables researchers to use the web as a corpus in order to investigate the dynamics of lexical innovation on a large-scale and systematic basis. The NeoCrawler represents an innovative web-mining tool which opens up new opportunities for linguists to tackle a number of unresolved and under-researched issues in the field of lexical innovation. This paper presents the design as well as the most important characteristics of two modules, the Discoverer and the Observer, with regard to the usage-based study of lexical innovation and diffusion.

Keywords: lexical innovation, neologisms, webcrawler, data-mining, string matching, innovation identification
Article outline
  • 1.Introduction
  • 2.The Discoverer
    • 2.1Source material and pre-processing
    • 2.2String matching procedure
    • 2.3Reference dictionary
    • 2.4Manual evaluation
  • 3.The Observer
    • 3.1Architecture of the Observer
    • 3.2The NeoCrawler database
    • 3.3The Observer interface
  • 4.Summary and future work
  • Notes
  • References
Published online: 12 June 2019
DOI logo
https://doi.org/10.1075/pc.00006.ker
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Cited by

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Würschinger, Quirin
2021. Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter. Frontiers in Artificial Intelligence 4 DOI logo

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