Pragmatics and natural language generation
Artificial Intelligence
About reformulation in full-text IRS
Information Processing and Management: an International Journal
Automatic stochastic tagging of natural language texts
Computational Linguistics
A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
Improvising linguistic style: social and affective bases for agent personality
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
An Empirical Text Categorizing Computational Model Based on Stylistic Aspects
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Generating natural language under pragmatic constraints
Generating natural language under pragmatic constraints
A computational theory of goal-directed style in syntax
Computational Linguistics
Tracking point of view in narrative
Computational Linguistics
Automatic detection of text genre
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Recognizing text genres with simple metrics using discriminant analysis
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Overview of the second text retrieval conference (TREC-2)
HLT '94 Proceedings of the workshop on Human Language Technology
The Text REtrieval Conferences (TRECs)
TIPSTER '96 Proceedings of a workshop on held at Vienna, Virginia: May 6-8, 1996
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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.