Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2783258.2783361acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Reconstructing Textual Documents from n-grams

Published: 10 August 2015 Publication History

Abstract

We analyze the problem of reconstructing documents when we only have access to the n-grams (for n fixed) and their counts from the original documents. Formally, we are interested in recovering the longest contiguous substrings of whose presence in the original documents we are certain. We map this problem on a de Bruijn graph, where the n-grams form the edges and where every Eulerian cycles gives a plausible reconstruction. We define two rules that reduce this graph, preserving all possible reconstructions while at the same time increasing the length of the edge labels. From a theoretical perspective we prove that the iterative application of these rules gives an irreducible graph equivalent to the original one. We then apply this on the data from the Gutenberg project to measure the number and size of the obtained longest substrings. Moreoever, we analyze how the n-gram corpus could be noised to prevent reconstruction, showing empirically that removing low frequent n-grams has little impact. Instead, we propose another method consisting in adding strategically fictitious n-grams and show that a noised corpus like that is much harder to reconstruct while increasing only little the perplexity of a language model obtained through it.

Supplementary Material

MP4 File (p329.mp4)

References

[1]
Nicola Cancedda. Private access to phrase tables for statistical machine translation. In ACL, pages 23--27, 2012.
[2]
Rayan Chikhi, Antoine Limasset, Shaun Jackman, Jared Simpson, and Paul Medvedev. On the representation of de bruijn graphs. arXiv preprint arXiv:1401.5383, 2014.
[3]
Rayan Chikhi and Guillaume Rizk. Space-efficient and exact de bruijn graph representation based on a bloom filter. Algorithms for Molecular Biology, 8:22, 2013.
[4]
Phillip E C Compeau, Pavel A Pevzner, and Glenn Tesler. How to apply de Bruijn graphs to genome assembly. Nature biotechnology, 29(11):987--91, November 2011.
[5]
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms (3. ed.). MIT Press, 2009.
[6]
N. G. De Bruijn. A combinatorial problem. Koninklijke Nederlandse Akademie v. Wetenschappen, 49:758--764, 1946.
[7]
Nathanael Fillmore, Andrew B Goldberg, and Xiaojin Zhu. Document recovery from bag-of-word indices. Technical report, 2008.
[8]
Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, 1979.
[9]
Piyush Kansal and Himanshu Jindal. Reconstructing books using google n-grams. Master's thesis, Stony Brook University, 2011.
[10]
Yuri Lin, Jean-Baptiste Michel, Erez Aiden Lieberman, Jon Orwant, Will Brockman, and Slav Petrov. Syntactic annotations for the google books ngram corpus. In ACL (System Demonstrations), pages 169--174, 2012.
[11]
Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden. Quantitative analysis of culture using millions of digitized books. Science, 331(6014):176--182, 2011.
[12]
Jason R Miller, Sergey Koren, and Granger Sutton. Assembly algorithms for next-generation sequencing data. Genomics, 95(6):315--27, June 2010.
[13]
P a Pevzner, H Tang, and M S Waterman. An Eulerian path approach to DNA fragment assembly. Proceedings of the National Academy of Sciences of the United States of America, 98(17):9748--53, August 2001.
[14]
Pavel A Pevzner. 1-Tuple DNA Sequencing : Computer Analysis. Journal of Biomolecular, 1989.
[15]
Robert Tarjan. Depth-First Search and Linear Graph Algorithms. SIAM Journal on Computing, 1(2):146--160, June 1972.
[16]
Xiaojin Zhu, Andrew B Goldberg, Michael Rabbat, and Robert D Nowak. Learning bigrams from unigrams. In ACL, pages 656--664, 2008.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2015
2378 pages
ISBN:9781450336642
DOI:10.1145/2783258
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. de bruijn graph
  2. eulerian cycles
  3. privacy-preserving data mining

Qualifiers

  • Research-article

Conference

KDD '15
Sponsor:

Acceptance Rates

KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 800
    Total Downloads
  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 23 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media