Suchergebnisse

Corpus linguistics has been closely intertwined with digital technology since the introduction of university computer mainframes in the 1960s. Making use of both digitized data in the form of the language corpus and computational methods of analysis involving concordancers and statistics software, corpus linguistics arguably has a place in the digital humanities. Still, it remains obscure and figures only sporadically in the literature on the digital humanities. This article provides an overview of the main principles of corpus linguistics and the role of computer technology in relation to data and method and also offers a bird's-eye view of the history of corpus linguistics with a focus on its intimate relationship with digital technology and how digital technology has impacted the very core of corpus linguistics and shaped the identity of the corpus linguist. Ultimately, the article is oriented towards an acknowledgment of corpus linguistics' alignment with the digital humanities.

Trademark law recognizes that the same word can mean different things in different commercial contexts. Legal protection might extend to two or more owners who use the same symbol (like Delta) to indicate different sources of disparate goods or services, such as airlines and faucets. Generally, only those uses that threaten to confuse consumers—the use of similar symbols on identical or related goods—are subject to legal sanction. But the law extends special protection to famous trademarks, not only against confusing use, but also against dilution: non-confusing use that blurs or tarnishes the distinctiveness of the famous mark. The result of protection against blurring is that the law treats the famous mark as if the sole proper use of the term in the commercial context is to designate goods and services from the famous mark's owner. Protection against dilution extends only to famous marks, but courts and scholars apply differing standards for assessing fame. Nonetheless, the trend over time has been to treat fame as a threshold requiring both sufficient renown—the famous mark must be a household name—and relatively singular use. This article argues that corpus linguistic analysis can provide evidence of whether a mark is sufficiently prominent and singular to qualify for anti-dilution protection. Corpus linguistics detects language patterns and meaning from analyzing actual language use. This article draws data primarily from two large, publicly accessible databases (corpora) to investigate whether litigated trademarks are both prominent and unique. Courts and parties can consider frequency evidence to establish or refute prominence, and contextual evidence like concordance and collocation to establish relative singularity. Corpus evidence has some advantages over standard methods of assessing fame. Corpus evidence is cheaper to generate than survey evidence but may be equally probative. Corpus analysis can help right-size dilution litigation: A litigant could estimate the prominence and singularity of an allegedly famous mark using corpus evidence prior to discovery and better predict whether the mark should qualify for anti-dilution protection. Judges should be able to rely on the results of corpus analysis with reasonable confidence. Additionally, corpus evidence can show use of a mark over time, providing courts with tools to assess when a mark first became famous, a question that a survey generated for litigation cannot readily answer.

Introduction -- Part I: Corpus design, corpus methods and corpus types -- Chapter 1. Corpus Compilation (Annelie Adel) -- Chapter 2. Corpus Annotation (John Newman & Christopher Cox) -- Chapter 3. Corpus Architecture (Amir Zeldes) -- Chapter 4. Analyzing frequency lists (Don Miller) -- Chapter 5. Analyzing dispersion (Stefan Th. Gries) -- Chapter 6. Analyzing keyword lists (Paul Rayson & Amanda Potts) -- Chapter 7. Analyzing co-occurrence data (Stefan Th. Gries & Phil Durrant) -- Chapter 8. Analyzing concordances (Steffi Wulff & Paul Baker) -- Chapter 9. Programming for corpus linguistics (Laurence Anthony) -- Chapter 10. Diachronic corpora (Kristine Davidse & Henrik de Smet) -- Chapter 11. Spoken corpora (Ulrike Gut) -- Chapter 12. Parallel corpora (Marie-Aude Lefer) -- Chapter 13. Learner corpora (Gaëtanelle Gilquin) -- Chapter 14. Child-language corpora (Sabine Stoll & Robert Schikowski) -- Chapter 15. Web corpora (Andrew Kehoe) -- Chapter 16. Multimodal corpora (Dawn Knight & Svenja Adolphs) -- Part II: Quantitative analyses of corpora with R -- Chapter 17. Descriptive statistics and visualization with R (Magali Paquot & Tove Larsson) -- Chapter 18. Cluster analysis (Hermann Moisl) -- Chapter 19. Multidimensional exploratory approaches (Guillaume Desagulier) -- Chapter 20. Classical monofactorial (parametric and non-parametric) tests (Vaclav Brezina) -- Chapter 21. Fixed-effects regression modeling (Martin Hilpert & Damian Blasi) -- Chapter 22. Mixed-effects regression modeling (Roland Schäfer) -- Chapter 23. Generalized additive mixed models (R. Harald Baayen & Maja Linke) -- Chapter 24. Bootstrapping techniques (Jesse Egbert & Luke Plonsky) -- Chapter 25. Conditional inference trees and random forests (Natalia Levshina) -- Part III: Pulling everything together -- Chapter 26. Writing up a corpus linguistic paper (Stefan Th. Gries & Magali Paquot) -- Chapter 27. Meta-analyzing corpus linguistic research (Atsushi Mizumoto, Luke Plonsky & Jesse Egbert) -- Index.

This book explores the structure and use of academic and professional discourse through the lens of corpus linguistics. The goal of this book is to show how insights from corpus linguistic analyses can help us better understand how we use academic and professional language and help us find ways to better train newcomers to the genres used in various professional contexts. The contributions to this book show that specialized corpora of specific genres from a variety of fields allow us to make more relevant observations about the function and use of language for particular purposes. The specia.

Corpus linguistics has much to offer history, being as both disciplines engage so heavily in analysis of large amounts of textual material. This book demonstrates the opportunities for exploring corpus linguistics as a method in historiography and the humanities and social sciences more generally. Focusing on the topic of prostitution in 17th-century England, it shows how corpus methods can assist in social research, and can be used to deepen our understanding and comprehension. McEnery and Baker draw principally on two sources – the newsbook Mercurius Fumigosis and the Early English Books Online Corpus. This scholarship on prostitution and the sex trade offers insight into the social position of women in history

Since the 1980's and the rise of computer-assisted technologies, Corpus Linguistics (CL) has become a mainstream methodology in linguistics, making it possible to analyze 'very extensive collections of transcribed utterances or written texts' (McEnery & Hardie, 2012: i). This workshop will be devoted to main theoretical and methodological basics of Corpus Linguistics. It will be composed of three main parts. Firstly, we will address the process of corpus construction, with a focus on data collection, balance and representativeness. Secondly, we will discuss essential notions of CL, such as tokens, types, concordances, collocations, corpus annotation and the distinction between corpus-based and corpus-driven approaches. Finally, we will present various types of specialized corpora (for example, monolingual and bilingual corpora, learner corpora and political corpora) to give an overview of the research questions that can be addressed thanks to Corpus Linguistics in a variety of disciplines. This workshop will also include a hands-on session during which the participants will have the opportunity to apply the notions that have been discussed to their own corpus, using the free corpus processing softwares AntConc (http://www.laurenceanthony.net/software/antconc, Anthony, 2019) and Unitex (https://unitexgramlab.org, Paumier, 2020). ; Peer reviewed