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Machine-readable text corpora and the linguistic description of languages

[conference paper]


This document is a part of the following document:
Text analysis and computers

Mair, Christian

Corporate Editor
Zentrum für Umfragen, Methoden und Analysen -ZUMA-

Abstract

"To understand the role of machine-readable text corpora in linguistics it is necessary to consider the four possible sources of data for the linguist, viz. (1) the analyst's own introspection/ intuition, (2) more or less systematically conducted elicitation experiments with groups of native speaker... view more

"To understand the role of machine-readable text corpora in linguistics it is necessary to consider the four possible sources of data for the linguist, viz. (1) the analyst's own introspection/ intuition, (2) more or less systematically conducted elicitation experiments with groups of native speakers of the language studied, (3) collections of authentic spoken or written citations gathered unsystematically, and (4) evidence extracted systematically from a well-defined corpus of texts. After a discussion of the advantages and disadvantages of the various sources of data, I will briefly exemplify recent advances made in the corpus-based description of languages that have become possible as a result of the application of computer technology to linguistics and then go on to present the major databases currently available for the study of English and German." (author's abstract)... view less

Keywords
text analysis; language; computational linguistics; data capture

Classification
Science of Literature, Linguistics

Collection Title
Text analysis and computers

Editor
Züll, Cornelia; Harkness, Janet; Hoffmeyer-Zlotnik, Jürgen H. P.

Conference
Text Analysis and Computers Conference. Mannheim, 1995

Document language
English

Publication Year
1996

City
Mannheim

Page/Pages
p. 64-75

Series
ZUMA-Nachrichten Spezial, 1

ISBN
3-924220-11-5

Status
Published Version; reviewed

Licence
Deposit Licence - No Redistribution, No Modifications


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.