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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.

This paper deals with the problems stemming from the parsing of long sentences in quasi free word order languages. Due to the word order freedom of a large category of languages including Greek and the limitations of rule-based grammar parsers in parsing unrestricted texts of such languages, we propose a flexible and effective method for parsing long sentences of such languages that combines heuristic information and pattern-matching techniques in early processing levels. This method is deeply characterized by its simplicity and robustness. Although it has been developed and tested for the Greek language, its theoretical background, implementation algorithm and results are language independent and can be of considerable value for many practical natural language processing (NLP) applications involving parsing of unrestricted texts.

The presented work is strongly motivated by the need for categorizing unrestricted text in terms of a functional style (FS) in order to attain a satisfying outcome in style processing. Towards this aim, a three level description of FS is given that comprises: (a) the basic categories of FS; (b) the main features that characterize each one of the above categories; and (c) the linguistic identifiers that act as style markers in text for the identification of the above features. Special emphasis is put on the problems that faced the computational implementation of the aforementioned findings, as well as the selection of the most appropriate stylometrics (i.e., stylistic scores) to achieve better results on text categorization. This approach is language independent, empirically driven, and can be used in various applications including grammar and style checking, natural language generation, style verification in real world text, and recognition of style shift between adjacent portions of text.

Free word-order languages cause problems when being analysed due to their great composing power and flexibility. The paper deals with the problems stemming from the analysis of complex sentences as well as with the adoption of suitable grammars to parse them in free word-order languages. Based on a careful study of large Greek corpora, we propose a method for parsing complex sentences in such languages that combines heuristic information and pattern matching techniques in early processing levels. Although this method has been implemented and tested in the Greek language, its theoretical background and results are language independent and can be proved useful for many practical applications. >

A hybrid model for representing knowledge in a Greek natural language interface to MS-DOS is presented. This model employs semantic knowledge of the operating system domain to develop a hybrid architecture integrating symbolic and connectionist modules for recognizing the MS-DOS command labels of related or unrelated successive natural language user messages. This approach was deeply motivated by handling ellipsis, ambiguity and flexibility of sentence structure as well as possible. A comparative experimental evaluation of this approach to a symbolic one illustrating their pros and cons is also given. Finally, ways of extending this model are proposed with the view of making the natural language interface more user friendly and functional.

Current Information Extraction (IE) systems extract, in most cases, fixed information from documents [1,2]. This information pertains only to four distinct tasks: named entity recognition, coreference identification, template elements filling, and scenario-based template elements filling. Thus, providing these systems with the capability of locating stylistic features in a text and thus detecting its genre, it would be possible to meet specific user interests. For instance, users are often looking for texts on a certain topic with particular, quite narrow generic properties, such as authoritatively written documents, opinion pieces, scientific articles, and so on.