Meaning construction in popular science : an investigation into cognitive, digital, and empirical approaches to discourse reification
This thesis uses cognitive linguistics and digital humanities techniques to analyse abstract conceptualization in a corpus of popular science texts. Combining techniques from Conceptual Integration Theory, corpus linguistics, data-mining, cognitive pragmatics and computational linguistics, it presents a unified approach to understanding cross-domain mappings in this area, and through case studies of key extracts, describes how concept integration in these texts operates. In more detail, Part I of the thesis describes and implements a comprehensive procedure for semantically analysing large bodies of text using the recently- completed database of the Historical Thesaurus of English. Using log-likelihood statistical measures and semantic annotation techniques on a 600,000 word corpus of abstract popular science, this part establishes both the existence and the extent of significant analogical content in the corpus. Part II then identifies samples which are particularly high in analogical content from the corpus, and proposes an adaptation of empirical and corpus methods to support and enhance conceptual integration (sometimes called conceptual blending) analyses, informed by Part Iâs methodologies for the
Alexander, Marc Gabriel
P Philology. Linguistics
Meaning construction in popular science : an investigation into cognitive, digital, and empirical approaches to discourse reification. This thesis uses cognitive linguistics and digital humanities techniques to analyse abstract conceptualization in a corpus of popular science texts. Combining techniques from Conceptual Integration Theory, corpus linguistics, data-mining, cognitive pragmatics and computational linguistics, it presents a unified approach to understanding cross-domain mappings in this area, and through case studies of key extracts, describes how concept integration in these texts operates. In more detail, Part I of the thesis describes and implements a comprehensive procedure for semantically analysing large bodies of text using the recently- completed database of the Historical Thesaurus of English. Using log-likelihood statistical measures and semantic annotation techniques on a 600,000 word corpus of abstract popular science, this part establishes both the existence and the extent of significant analogical content in the corpus. Part II then identifies samples which are particularly high in analogical content from the corpus, and proposes an adaptation of empirical and corpus methods to support and enhance conceptual integration (sometimes called conceptual blending) analyses, informed by Part Iâs methodologies for the
meaning construction popular_science investigation cognitive digital empirical approach discourse reification cognitive_linguistics digital humanity technique analyse abstract conceptualization corpus popular_science text combining technique conceptual integration theory corpus_linguistics data mining cognitive pragmatic computational_linguistics present unify cross domain mapping area case_study key extract describes concept integration text operate detail part describe implement comprehensive procedure semantically analyse large_body text use recently complete database historical_thesaurus english using log likelihood statistical measure semantic annotation technique word corpus abstract popular_science part establishes existence extent significant analogical content corpus part identify sample particularly high analogical content corpus propose adaptation empirical corpus method support enhance conceptual integration sometimes call conceptual analyse informed part methodology