UZH-Logo

Linguistic support for revising and editing


Mahlow, C; Piotrowski, M (2008). Linguistic support for revising and editing. In: Gelbukh, A. Computational Linguistics and Intelligent Text Processing. Berlin / Heidelberg, Germany: Springer, 631-642.

Abstract

Revising and editing are important parts of the writing process. In fact, multiple revision and editing cycles are crucial for the production of high-quality texts. However, revising and editing are also tedious and error-prone, since changes may introduce new errors.
Grammar checkers, as offered by some word processors, are not a solution. Besides the fact that they are only available for few languages, and regardless of the questionable quality, their conceptual approach is not suitable for experienced writers, who actively create their texts. Word processors offer few, if any, functions for handling text on the same cognitive level as the author: While the author is thinking in high-level linguistic terms, editors and word processors mostly provide low-level character oriented functions. Mapping the intended outcome to these low-level operations is distracting for the author, who now has to focus for a long time on small parts of the text. This results in a loss of global overview of the text and in typical revision errors (duplicate verbs, extraneous conjunctions, etc.)
We therefore propose functions for text processors that work on the conceptual level of writers. These functions operate on linguistic elements, not on lines and characters. We describe how these functions can be implemented by making use of NLP methods and linguistic resources.

Revising and editing are important parts of the writing process. In fact, multiple revision and editing cycles are crucial for the production of high-quality texts. However, revising and editing are also tedious and error-prone, since changes may introduce new errors.
Grammar checkers, as offered by some word processors, are not a solution. Besides the fact that they are only available for few languages, and regardless of the questionable quality, their conceptual approach is not suitable for experienced writers, who actively create their texts. Word processors offer few, if any, functions for handling text on the same cognitive level as the author: While the author is thinking in high-level linguistic terms, editors and word processors mostly provide low-level character oriented functions. Mapping the intended outcome to these low-level operations is distracting for the author, who now has to focus for a long time on small parts of the text. This results in a loss of global overview of the text and in typical revision errors (duplicate verbs, extraneous conjunctions, etc.)
We therefore propose functions for text processors that work on the conceptual level of writers. These functions operate on linguistic elements, not on lines and characters. We describe how these functions can be implemented by making use of NLP methods and linguistic resources.

Citations

Altmetrics

Downloads

185 downloads since deposited on 12 Dec 2008
26 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Uncontrolled Keywords:computational linguistics, natural language processing, interactive editing
Language:English
Date:2008
Deposited On:12 Dec 2008 13:05
Last Modified:05 Apr 2016 12:37
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:4919
ISSN:0302-9743 (P) 1611-3349 (E)
ISBN:978-3-540-78134-9
Additional Information:9th international conference : proceedings / CICLing 2008, Haifa, Israel, February 17-23, 2008
Publisher DOI:10.1007/978-3-540-78135-6_54
Official URL:http://dx.doi.org/10.1007/978-3-540-78135-6_54
Related URLs:http://opac.nebis.ch/F/?local_base=NEBIS&con_lng=GER&func=find-b&find_code=SYS&request=005573080
http://lingured.info/ (Author)
Permanent URL: http://doi.org/10.5167/uzh-6542

Download

[img]
Preview
Content: Accepted Version
Filetype: PDF
Size: 1MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations