Enhancing text retrieval by using advanced stylistic techniques

Text retrieval techniques have long focused on the topic of texts rather than the pragmatic role they play per se. In this article, we address two other aspects in text processing that could enhance text retrieval: (a) the detection of functional style in retrieved texts, and (b) the detection of writer's attitude towards a given topic in retrieved texts. The former is justified by the fact that current text databases have become highly heterogeneous in terms of document inclusion, while the latter is dictated by the need for advanced and intelligent retrieval tools. Towards this aim, two generalised methodologies are presented in order to achieve the implementation of the findings in both aspects in text processing respectively. Particularly, the first one is fully developed and thus is analysed and evaluated in detail, while for the second one the theoretical framework is given for its subsequent computational implementation. Both approaches are as language independent as possible, empirically driven, and can be used, apart from information retrieval purposes, in various natural language processing applications. These include grammar and style checking, natural language generation, summarisation, style verification in real-world texts, recognition of style shift between adjacent portions of text, and author identification.