Comparing LancsBox and AntConc in the extraction of Passives and Nominals: Towards Objectivity in Critical Discourse Analysis

By Hanane ZIH, Maha El Biadi, Zakariyae Chatri

Abstract


Advancements in information technology have recently proved essential in a variety of disciplines. Linguistics in general, and Critical Discourse Analysis in particular are no exception, as practitioners working within these areas believe that drawing upon certain computerized methods enables them to obtain accurate results from which they can draw valid generalizations. Natural language processing programs geared for the analysis of large linguistic corpus have exponentially proliferated thanks to machine-readable texts. Critical discourse analysts have become more interested in adopting corpus linguistic approaches to discourse analysis.   This variety of programs, however, leads one to wonder about their level of accuracy and effectiveness in identifying the specific linguistic features of interest to the researcher, and their ability to reach the same level of preciseness that a manual analysis of the texts can achieve, allowing a more manageable and objective analysis to be undertaken. The present paper aims, therefore, to compare two widely renowned corpus linguistics programs to analyze linguistic features in the area of textual analysis to find out about the extent to which there are similarities between them, in terms of the findings they yield. It will draw some comparisons between two programs, namely LancsBox and AntConc, in order to find out about their effectiveness in culling the linguistic features such as passive and nominalized constructions from a large scale of linguistic data.  We will after that proceed to comparing them to the results obtained through a manual analysis of the data to see if there are any differences as well as the extent to which critical discourse analysis combined with corpus linguistic methods can offer more objective and reliable results, refuting by this the common cited criticism of attempting to prove a preconceived point.  A corpus of ten news articles published in The Times online newspaper were downloaded and analyzed both manually and digitally so as to examine the occurrences and the distribution of the two aforementioned grammatical constructions in the news reports. The findings show that corpus linguistic software can reliably extract passives and active instances from the texts. Although both LancsBox and AntConc revealed approximately the same frequencies compared to the findings we obtained manually, both programs did not help in specifically isolating the clauses which report passivized and nominalized actions performed by the perpetrator. Therefore, the study concluded that while the corpus linguistic software can facilitate the identification of frequent and salient linguistic patterns especially in a large scale of data, the human interpretation is mandatory as far as the research purpose is concerned.

Keywords: AntConc, Corpus linguistics Software, Critical Discourse Analysis, Grammatical Structure, LancsBox, Nominalization, Passivisation.


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