Artificial sequences and complexity measures

Abstract

In this paper we exploit concepts of information theory to address the fundamental problem of identifying and defining the most suitable tools to extract, in a automatic and agnostic way, information from a generic string of characters. We introduce in particular a class of methods which use in a crucial way data compression techniques in order to define a measure of remoteness and distance between pairs of sequences of characters (e.g. texts) based on their relative information content. We also discuss in detail how specific features of data compression techniques could be used to introduce the notion of dictionary of a given sequence and of Artificial Text and we show how these new tools can be used for information extraction purposes. We point out the versatility and generality of our method that applies to any kind of corpora of character strings independently of the type of coding behind them. We consider as a case study linguistic motivated problems and we present results for automatic language recognition, authorship attribution and self consistentclassification.

The Language of Coalition Formation in Online Multiparty Negotiations

“…Compression algorithms are especially efficient when examining natural language because they contain so many redundancies (Brillouin, 2004). Scholars have demonstrated how zipping can be used in measuring language similarity between two or more texts (Baronchelli, Caglioti, & Loreto, 2005) and how compression algorithms and entropybased approaches are useful to measure online texts (Gordon, Cao, & Swanson, 2007;Huffaker, Jorgensen, Iacobelli, Tepper, & Cassell, 2006;Nigam, Lafferty, & McCallum, 1999;Schneider, 1996). In our study, entropy represents the number of similar linguistic choices at both word and phrase levels.…”

Computational measures for language similarity across time in online communities

“…More recently, researchers have experimented with data compression algorithms as a measure of document complexity and similarity. This technique uses compression ratios as an approximation of a document's information entropy (Baronchelli, Caglioti, & Loreto, 2005;Benedetto, Caglioti, & Loreto, 2002). Standard Zipping algorithms have demonstrated effectiveness in a variety of document comparison and classification tasks.…”

Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning

“…For a given compressor, and two objects (e.g. bit-strings) of equal length, the object with the shortest compressed representation can be considered less complex as it contains more identifiable regularity [1].…”