Information retrieval systems are built to handle texts as topical items: texts are tabulated by occurrence frequencies of content words in them, under the assumption that text topic is reasonably well modeled by content word occurrence. But texts have several interesting characteristics beyond topic. The experiments described in this text investigate stylistic variation. Roughly put, style is the difference between two ways of saying the same thing -- and systematic stylistic variation can be used to characterize the genre of documents. These experiments investigate if stylistic information is distinguishable using simple language engineering methods, and if in that case this type of information can be used to improve information retrieval systems.

. Natural language processing techniques may hold a tremendous potential for overcoming the inadequacies of purely quantitative methods of text information retrieval, but the empirical evidence to support such predictions has thus far been inadequate, and appropriate scale evaluations have been slow to emerge. In this chapter, we report on the progress of the Natural Language Information Retrieval project, a joint effort of several sites led by GE Research, and its evaluation in the 6th Text Retrieval Conferences (TREC-6). 1. Introduction and Motivation Recently, we noted a renewed interest in using NLP techniques in information retrieval, sparked in part by the sudden prominence, as well as the perceived limitations, of existing IR technology in rapidly emerging commercial applications, including on the Internet. This has also been reflected in what is being done at TREC: using phrasal terms and proper name annotations became a norm among TREC participants, and a special interest tra...

. Texts exhibit considerable stylistic variation. This paper reports an experiment where a corpus of documents (N= 75 000) is analyzed using various simple stylistic metrics. A subset (n = 1000) of the corpus has been previously assessed to be relevant for answering given information retrieval queries. The experiment shows that this subset differs significantly from the rest of the corpus in terms of the stylistic metrics studied. 1 Introduction Texts vary not only by topic. Stylistic variation between texts of the same topic is often at least as noticeable as the topical variation between texts of different topic but same genre or variety; style is, broadly defined, the difference between two ways of saying the same thing. Stylistic variation in a given text, given the liberal definition above, can occur in many ways and on many linguistic levels: lexical choice, choice of syntactic structures, choice of cohesion markers on a textual level, and so forth. Some choices are constrained ...

ing and Summarization : : : : : : : : : : : : : : : : : : : 19 1.4.8 How textuality could be utilized better : : : : : : : : : : : : : : : 19 1.5 Requests and queries: Dialog : : : : : : : : : : : : : : : : : : : : : : : : : 20 1.5.1 Boolean and probablistic approaches : : : : : : : : : : : : : : : : 20 1.6 Information Access Processes: Texts more than are : : : : : : : : : : : : 21 1.6.1 Typical query processing : : : : : : : : : : : : : : : : : : : : : : : 21 1.6.2 Beyond single queries : : : : : : : : : : : : : : : : : : : : : : : : : 1.6.3 Query expansion : : : : : : : : : : : : : : : : : : : : : : : : : : : 22 1.6.4 Relevance Feedback : : : : : : : : : : : : : : : : : : : : : : : : : : 23 1.6.5 Other qualities of text : : : : : : : : : : : : : : : : : : : : : : : : 23 1.7 Texts are sometimes written in other languages than English : : : : : : : 23 1.8 The contribution to linguistics : : : : : : : : : : : : : : : : : : : : : : : : 24 1.9 Conclusions: open research questions : : :...