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Кукушкина, О.В.; Polikarpov, A. A.; Khmelev, Dmitry V.

doi:10.1023/a:1010478226705

Cited by 84 publications

(22 citation statements)

References 7 publications

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“…The generic claim made in [1] that the Markov chain approach outperforms LZ-based schemes is then not so general. On the other hand, in our opinion, even the claim that Markov chain approaches outperform gzip-based schemes is not well supported by the experiments presented in [3].In [3] the authors used a method which is slightly but crucially different from ours [2]. The authorship attribution of a text X is performed by comparing this file with one very long file per author to be used as the reference file (created by simply appending all the files of each single reference author in one single file) whose length is between 140 Kb and 6.9 Mb (average length 1.2 Mb).…”

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Benedetto, Caglioti, and Loreto Reply: In [1] Khmelev et al claim that [2] contains many misleading statements and that Markov chain approaches represent a more attractive technique than Lempel-Ziv (LZ)-based compression schemes.The authors recall the results of the experiments reported in [3] where it is shown that Markov chain based methods outperform gzip based methods. First of all, it is important to remember that in [3] an LZ-based scheme (rarw) outperformed the Markov chain approach.

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confidence: 82%

“…The authors recall the results of the experiments reported in [3] where it is shown that Markov chain based methods outperform gzip based methods. First of all, it is important to remember that in [3] an LZ-based scheme (rarw) outperformed the Markov chain approach.…”

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“…In order to compare languages written with different alphabets one should, for instance, consider texts written with the phonetic alphabet. This is the reason for not having included in our preliminary analysis of the language tree languages such as Chinese, Greek, Russian, etc.As for the computational time we agree that data compression techniques can be slower than other procedures, but in our opinion the potentiality of the method is not only related to its speed.Finally, we remark that only after the publication of our Letter did we become aware of the results by Loewenstern et al[6] and of Khmelev [3] in which the idea of zipping a file B appended to a file A in order to define a remoteness between A and B had been previously stated. …”

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Benedetto, Caglioti, and Loreto Reply:

2003

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Benedetto, Caglioti, and Loreto Reply: In [1] Khmelev et al. claim that [2] contains many misleading statements and that Markov chain approaches represent a more attractive technique than Lempel-Ziv (LZ)-based compression schemes.The authors recall the results of the experiments reported in [3] where it is shown that Markov chain based methods outperform gzip based methods. First of all, it is important to remember that in [3] an LZ-based scheme (rarw) outperformed the Markov chain approach. The generic claim made in [1] that the Markov chain approach outperforms LZ-based schemes is then not so general. On the other hand, in our opinion, even the claim that Markov chain approaches outperform gzip-based schemes is not well supported by the experiments presented in [3].In [3] the authors used a method which is slightly but crucially different from ours [2]. The authorship attribution of a text X is performed by comparing this file with one very long file per author to be used as the reference file (created by simply appending all the files of each single reference author in one single file) whose length is between 140 Kb and 6.9 Mb (average length 1.2 Mb). This procedure seems to us to be definitively wrong in the perspective of using the gzip compression scheme. Gzip has a sliding window of 32 Kb over which it looks for the longest matchings. Now, using as reference file a file longer than 32 Kb and appending to it the file X, only the last 32 Kb (which depend on the way the reference file has been created) are compared with the unknown file. Therefore most of the available information is lost. In our method [2] we use all the available files as reference files and we compare the unknown file X with all the other files. The indication of the authorship is given by the reference text closest to the unknown text.In order to show how this slight difference in the procedure could bring drastically different results we have performed two sets of experiments. The first experiment concerns the classification of a corpus of newsgroup messages widely used for the comparison of different approaches [4]. The rate of success is 60% with the method of [3] and 85% with our method. The second experiment concerned the data presented in [2]. In this case our prescription allows for 93% (84=90) of success, while with the prescription adopted in [3] we get a success rate of 77% (69=90).As for the usefulness of compression techniques for DNA analysis, in our opinion this is a very challenging field where it is almost impossible at present to say which method will be asymptotically successful. Just to give some examples, in [5] a method has been proposed to compress DNA sequences based on the backward search of approximate repeats. This allows one to define a distance between sequences useful to compare different genomes and build Phylogenetic trees.As for our definition of remoteness [Eq. (1) in [2] ] it is important to stress that relative entropy is a positive defined object. Since data compression schemes provide an estimate of...

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Benedetto, Caglioti, and Loreto Reply:

2003

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“…Moreover in the field of the so-called computational linguistics there have been several contributions showing how data compression techniques could be useful in solving different problems (an incomplete list would include [14,15,16,17,18,19,20,21]): language recognition, authorship recognition or attribution, language classification, classification of large corpora by subject, etc. But of course the possibilities of data-compression based methods go beyond computational linguistics.…”

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Data compression and learning in time sequences analysis

Puglisi

Benedetto

Caglioti

et al. 2003

Physica D: Nonlinear Phenomena

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Motivated by the problem of the definition of a distance between two sequences of characters, we investigate the so-called learning process of typical sequential data compression schemes. We focus on the problem of how a compression algorithm optimizes its features at the interface between two different sequences A and B while zipping the sequence A + B obtained by simply appending B after A. We show the existence of a universal scaling function (the so-called learning function) which rules the way in which the compression algorithm learns a sequence B after having compressed a sequence A. In particular it turns out that it exists a crossover length for the sequence B, which depends on the relative entropy between A and B, below which the compression algorithm does not learn the sequence B (measuring in this way the relative entropy between A and B) and above which it starts learning B, i.e. optimizing the compression using the specific features of B. We check the scaling function on three main classes of systems: Bernoulli schemes, Markovian sequences and the symbolic dynamic generated by a non trivial chaotic system (the Lozi map). As a last application of the method we present the results of a recognition experiment, namely recognize which dynamical systems produced a given time sequence. We finally point out the potentiality of these results for segmentation purposes, i.e. the identification of homogeneous sub-sequences in heterogeneous sequences (with applications in various fields from genetic to time-series analysis).

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Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

462

278

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Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample.In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

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Cited by 84 publications

References 7 publications

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Data compression and learning in time sequences analysis

Computational methods in authorship attribution

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