JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Features of an Error Correction Memory to Enhance Technical Texts Authoring in LELIE
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Features of an Error Correction Memory to Enhance Technical Texts Authoring in LELIE
SAINT-DIZIER, Patrick;
  PDF(new window)
 Abstract
In this paper, we investigate the notion of error correction memory applied to technical texts. The main purpose is to introduce flexibility and context sensitivity in the detection and the correction of errors related to Constrained Natural Language (CNL) principles. This is realized by enhancing error detection paired with relatively generic correction patterns and contextual correction recommendations. Patterns are induced from previous corrections made by technical writers for a given type of text. The impact of such an error correction memory is also investigated from the point of view of the technical writer's cognitive activity. The notion of error correction memory is developed within the framework of the LELIE project an experiment is carried out on the case of fuzzy lexical items and negation, which are both major problems in technical writing. Language processing and knowledge representation aspects are developed together with evaluation directions.
 Keywords
error correction memory;controlled natural languages;natural language processing;logic programming;
 Language
English
 Cited by
 References
1.
Alred, G. J., Charles T. B., & Walter, E. O. (2012). Handbook of Technical Writing. St Martin's Press, New York.

2.
Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F., & Gnaga, R. (2013). Automatic Checking of Conformance to Requirement Boilerplates via Text Chunking: An Industrial Case Study, 7th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement(ESEM 2013). Baltimore, MD, USA.

3.
Baader, F., & Nipkow, T. (1998). Term Rewriting and All That. Cambridge University Press.

4.
Barcellini, F., Albert, C., & Saint-Dizier, P. (2012). Risk Analysis and P revention: LELIE, a Tool dedicated to P rocedure and Requirement Authoring, LREC, Istanbul.

5.
Boulle, L., & Mesic, M. (2005). Mediation: Principles Processes Practice, Australia. Butterworths.

6.
Buchholz, S. (2002). Memory-based grammatical relation finding, PhD, Tilburg.

7.
Croce, D., Moschitti, A., Basili, R., & Palmer, M. (2012). Verb Classification using Distributional Similarity in Syntactic and Semantic Structures, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, ACL-2012, Jeju Island, Korea.

8.
Cruse, A. (1986). lexical semantics. Cambridge university Press.

9.
Daelemans, W., & van Der Bosch, A. (2005). Memory-Based Language Processing, Cambridge.

10.
Fuchs, N. E., Kaljurand, K., & Kuhn, T. (2008). Attempto Controlled English for Knowledge Representation. In Cristina Baroglio, Piero A. Bonatti, Jan Maluszynski, Massimo Marchiori, Axel Polleres, and Sebastian Schaffert, editors, Reasoning Web, Fourth International Summer School 2008, Lecture Notes in Computer Science 5224(pp. 104-124). Springer.

11.
Fellbaum, C. (1998). WordNet An Electronic Lexical Database. The MIT Press.

12.
Fuchs, N. E. (2012). First-Order Reasoning for Attempto Controlled English, In Proceedings of the Second International Workshop on Controlled Natural Language (CNL 2010), Springer.

13.
Ganier, F., & Barcenilla, J. (2007). Considering users and the way they use procedural texts: some prerequisites for the design of appropriate documents. In D. Alamargot, P. Terrier and J. -M. Cellier (Eds), Improving the production and understanding of written documents in the workplace, Elsevier Publishers.

14.
Garnier, M. (2011). Correcting errors in N+N structures in the production of French users of English, EuroCall, Nottingham.

15.
Grady, J. O. (2006). System Requirements Analysis. Academic Press, USA.

16.
Horn, L. (2001). A natural history of negation, D. Hume series, University of Chicago Press.

17.
Hull, E., Jackson, K., & Dick, J. (2011). Requirements Engineering, Springer.

18.
Kuhn, T. (2013). A Principled Approach to Grammars for Controlled Natural Languages and Predictive Editors. Journal of Logic, Language and Information, 22(1).

19.
Kuhn, T. (2014). A Survey and Classification of Controlled Natural Languages. Computational Linguistics, 40(1).

20.
Llyod, J. W. (2013). Foundations of Logic Programming, Springer Verlag, 3rd edition.

21.
O'Brien, S. (2003). Controlling Controlled English. An Analysis of Several Controlled Language Rule Sets. Dublin City University report.

22.
Pfenning, F. (1992). Types in Logic Programming, MIT Press.

23.
Saint-Dizier, P. (2012). Processing Natural Language Arguments with the Platform. Journal of Argumentation and Computation, 3(2).

24.
Saint-Dizier, P. (2014). Challenges of Discourse Processing: the case of technical documents. Cambridge Scholars, UK.

25.
Schriver, K. A. (1989). Evaluating text quality: The continuum from text-focused to reader-focused methods. IEEE Transactions on Professional Communication, 32, 238-255. crossref(new window)

26.
Unwalla, M. (2004). AECMA Simplified English. Available at: Retrieved 2015.06.25.

27.
Van der Linden K. (1993). Speaking of Actions: choosing Rhetorical Status and Grammatical Form in Instructional Text Generation. PhD, Univ. of Colorado, USA.

28.
Weiss E. H. (2000). Writing remedies. Practical exercises for technical writing. Oryx Press.

29.
White, C., & Schwitter, R. (2009). An Update on P ENG Light. In: Pizzato, L., Schwitter, R. (eds.) Proceedings of ALTA 2009, Sydney, Australia, 80-88.

30.
Wyner, A., et ali. (2010). On Controlled Natural Languages: Properties and Prospects.