Manna, SukanyaGedeon, Tamas (Tom)2015-12-109783540691365http://hdl.handle.net/1885/39882In this paper, we propose a term association model which extracts significant terms as well as the important regions from a single document. This model is a basis for a systematic form of subjective data analysis which captures the notion of relatedness of different discourse structures considered in the document, without having a predefined knowledge-base. This is a paving stone for investigation or security purposes, where possible patterns need to be figured out from a witness statement or a few witness statements. This is unlikely to be possible in predictive data mining where the system can not work efficiently in the absence of existing patterns or large amount of data. This model overcomes the basic drawback of existing language models for choosing significant terms in single documents. We used a text summarization method to validate a part of this work and compare our term significance with a modified version of Salton's [1].Keywords: Administrative data processing; Computational linguistics; Data structures; Decision support systems; Extraction; Information management; Information retrieval systems; Intersymbol interference; Knowledge based systems; Knowledge management; Modal analysi Gain of Sentences; Gain of Words; Information retrieval; Investigation; Summarization; Term significance200810.1007/978-3-540-69304-8_22015-12-09