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Genre analysis: English in academic and research settings / John M. Swales

01 Jan 1991-Vol. 1991, Iss: 1991, pp 1-99
About: The article was published on 1991-01-01 and is currently open access. It has received 5640 citations till now.
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Journal ArticleDOI
TL;DR: This paper conducted rhetorical analysis of academic conflict drawing on Hunston's (1993) framework and then applied Martin and White's (2005) engagement system to closely examine the use of interpersonal resources in representing and negotiating conflict.

38 citations

Journal ArticleDOI
TL;DR: In this paper, a small-scale study examining science-focused crowdfunding proposals from Kickstarter.com is presented, which explores the rhetoric of these proposals with respect to traditional grant funding proposals in the sciences and aims to understand how the language of science may be imported into this popular genre.
Abstract: Crowdfunding is a novel mechanism for garnering monetary support from the online public, and increasingly it is being used to fund science. This article reports a small-scale study examining science-focused crowdfunding proposals from Kickstarter.com. By exploring the rhetoric of these proposals with respect to traditional grant funding proposals in the sciences, this study aims to understand how the language of science may be imported into this popular genre.

38 citations

01 Jan 2007
TL;DR: The authors presented the basic elements of a computational theory of discourse structure that simplifies and expands upon previous work by specifying the basic units a discourse comprises and the ways in which they can relate.
Abstract: This paper presents the basic elements of a computational theory of discourse structure that simplifies and expands upon previous work. By specifying the basic units a discourse comprises and the ways in which they can relate, an account of discourse structure provides the basis for an account of discourse meaning. The paper distinguishes three components of discourse structure: one linguistic, one intentional, and one attentional. The theory, although still incomplete, provides a basis for investigating the structure and meaning of discourse, as well as for constructing "discourse processing systems."

38 citations

Proceedings Article
01 May 2016
TL;DR: Comunicacio presentada a la Tenth International Conference on Language Resources and Evaluation (LREC 2016), celebrada els dies 23 a 28 de maig de 2016 a Portorož, Eslovenia.
Abstract: Comunicacio presentada a la Tenth International Conference on Language Resources and Evaluation (LREC 2016), celebrada els dies 23 a 28 de maig de 2016 a Portorož, Eslovenia.

38 citations

Proceedings Article
01 Jan 2004
TL;DR: The use of high-precisions strategies was investigated and the system returned only highly reliable documents so that some queries were not answered by the system.
Abstract: Summary Because of corruptions in the XML TREC Genomics collection, which were detected only some days before the submission deadline, we were not able to submit runs for the ad hoc retrieval task (task I), although relevance judgements made after polling were used to evaluate our approaches, and therefore this report mostly focuses on the text categorization task (task II: triage and annotation). Task I. Our approach uses thesaural resources (from the UMLS) together with a variant of the Porter stemmer for string normalization. Gene and Protein Entities (GPE) of the collection were simply marked up by dictionary look up during the indexing in order to avoid erroneous conflation: strings not found in the UMLS Specialist lexicon (augmented with various English lexical resources) were considered as GPE and were moderately overweighed. Two different weighting schemas were tested: first, a standard tf-idf with cosine normalization, second a weighting based on the deviation from randomness model. For indexing the Genomic collection, the following MEDLINE records were selected: article’s titles, MeSH and RN terms, and abstract fields. We investigated the use of high-precisions strategies and our system returned only highly reliable documents so that some queries were not answered by the system. Our best run achieved an average precision of 32% (ranked 6 out of 27 participants). The score was obtained using UMLS resources and GPE (Gene and Protein Entity) tagging together with a combination of a classical atc.ltn schema (following SMART notation) with a deviation from randomness [8] weighting: L(ne)C2 and KL for expansion.

38 citations