Skip to main content
SpringerLink
Log in

Knowledge-lean Paraphrase Identification Using Character-Based Features

  • Conference paper
  • First Online:
Book cover Artificial Intelligence and Natural Language (AINL 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 789))

Included in the following conference series:

Abstract

The paraphrase identification task has practical importance in the NLP community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods should help improve the performance of NLP applications, including machine translation, information retrieval, question answering, text summarization, document clustering and plagiarism detection, amongst others. We consider an approach to paraphrase identification that may be considered “knowledge-lean”. Our approach minimizes the need for data transformation and avoids the use of knowledge-based tools and resources. Candidate paraphrase pairs are represented using combinations of word- and character-based features. We show that SVM classifiers may be trained to distinguish paraphrase and non-paraphrase pairs across a number of different paraphrase corpora with good results. Analysis shows that features derived from character bigrams are particularly informative. We also describe recent experiments in identifying paraphrase for Russian, a language with rich morphology and free word order that presents a particularly interesting challenge for our knowledge-lean approach. We are able to report good results on a three-way paraphrase classification task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In order to download TuPC: https://osf.io/wp83a/.

  2. 2.

    A debatable pair arises where the decisions of the four annotators are equally divided between “paraphrase” and “non-paraphrase”.

  3. 3.

    http://ainlconf.ru/paraphraser.

  4. 4.

    http://matplotlib.org/.

References

  1. Agirre, E., et al.: Semeval-2012 task 6: A pilot on semantic textual similarity. In: Proceedings of the 6th International Workshop on Semantic Evaluation, in Conjunction with the First Joint Conference on Lexical and Computational Semantics, pp. 385–393 (2012)

    Google Scholar 

  2. Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textual entailment methods. Artif. Intell. Res. 38(1), 135–187 (2010)

    MATH  Google Scholar 

  3. Bannard, C., Callison-Burch, C.: Paraphrasing with bilingual parallel corpora. In: Proceedings of the 43th Annual Meeting on Association for Computational Linguistics, pp. 597–604 (2005)

    Google Scholar 

  4. Barron-Cedeno, A., et al.: Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Comput. Linguist. 39(4), 917–947 (2013)

    Article  Google Scholar 

  5. Barzilay, R., et al.: Information fusion in the context of multi-document summarization. In: Proceedings of ACL, pp. 550–557 (1999)

    Google Scholar 

  6. Barzilay, R., Lee, L.: Learning to paraphrase: an unsupervised approach using multiple-sequence alignment. In: Naacl-2003, pp. 16–23 (2003)

    Google Scholar 

  7. Blacoe, W., Lapata, M.: A comparison of vector-based representations for semantic composition. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), pp. 546–556 (2012)

    Google Scholar 

  8. Callison-Burch, C., et al.: Improved statistical machine translation using paraphrases. In: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL 2006), pp. 17–24 (2006)

    Google Scholar 

  9. Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

  10. Culicover, P.W.: Paraphrase generation and information retrieval from stored text. Mech. Transl. Comput. Linguist. 11(1–2), 78–88 (1968)

    Google Scholar 

  11. Das, D., Smith, N.A.: Paraphrase identification as probabilistic quasi-synchronous recognition. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: ACL-IJCNLP 2009, pp. 468–476 (2009)

    Google Scholar 

  12. Demir, S., et al.: Turkish paraphrase corpus. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012), pp. 4087–4091 (2012)

    Google Scholar 

  13. Dolan, W.B., et al.: Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004. Association for Computational Linguistics, Geneva (2004)

    Google Scholar 

  14. Dolan, W.B., Brockett, C.: Automatically constructing a corpus of sentential paraphrases. In: Proceedings of IWP, pp. 9–16. Asia Federation of Natural Language Processing (2005)

    Google Scholar 

  15. Duclaye, F., et al.: Using the web as a linguistic resource for learning reformulations automatically. In: Proceedings of the Third International Conference on Language Resources and Evaluation (LREC 2002), Las Palmas, Canary Islands, Spain, pp. 390–396 (2002)

    Google Scholar 

  16. Eyecioglu, A., Keller, B.: ASOBEK: Twitter paraphrase identification with simple overlap features and SVMs. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, pp. 64–69 (2015)

    Google Scholar 

  17. Eyecioglu, A., Keller, B.: Constructing a Turkish corpus for paraphrase identification and semantic similarity. In: Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics. LNCS, pp. 562–574 (2016)

    Google Scholar 

  18. Fellbaum, C.: WordNet. An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  19. Fernando, S., Stevenson, M.: A semantic similarity approach to paraphrase detection. In: Proceedings of the 11th Annual Research Colloquium of the UK Special Interest Group for Computational Linguistics, pp. 45–52 (2008)

    Google Scholar 

  20. Finch, A., et al.: Using Machine translation evaluation techniques to determine sentence-level semantic equivalence. In: Proceedings of the Third International Workshop on Paraphrasing (IWP 2005), pp. 17–24 (2005)

    Google Scholar 

  21. Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378–382 (1971)

    Article  Google Scholar 

  22. Ganitkevitch, J., et al.: Learning sentential paraphrases from bilingual parallel corpora for text-to-text generation, pp. 1168–1179. Computational Linguistics (2011)

    Google Scholar 

  23. He, W., et al.: Enriching SMT training data via paraphrasing. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, IJCNLP 2011, pp. 803–810. Asian Federation of Natural Language Processing (2011)

    Google Scholar 

  24. Hearst, M.A., Grefenstette, G.: Refining automatically-discovered lexical relations: combining weak techniques for stronger results. In: Statistically-Based Natural Language Programming Techniques, Papers from the 1992 AAAI Workshop, Menlo Park, CA, pp. 64–72 (1992)

    Google Scholar 

  25. Hsu, C.-W., et al.: A practical guide to support vector classification. BJU Int. 101(1), 1396–1400 (2008)

    Google Scholar 

  26. Hunter, J.D.: Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007)

    Article  Google Scholar 

  27. Ji, Y., Eisenstein, J.: Discriminative improvements to distributional sentence similarity. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 891–896. Association for Computational Linguistics, Seattle (2013)

    Google Scholar 

  28. Kim, Y., et al.: Character-aware neural language models. CoRR 1508.06615 (2015)

    Google Scholar 

  29. Kozareva, Z., Montoyo, A.: Paraphrase identification on the basis of supervised machine learning techniques. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds.) FinTAL 2006. LNCS (LNAI), vol. 4139, pp. 524–533. Springer, Heidelberg (2006). https://doi.org/10.1007/11816508_52

    Chapter  Google Scholar 

  30. Ling, W., et al.: Finding function in form: compositional character models for open vocabulary word representation. CoRR 1508.02096 (2015)

    Google Scholar 

  31. Lintean, M., Rus, V.: Dissimilarity kernels for paraphrase identification. In: Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference, Palm Beach, FL, pp. 263–268 (2011)

    Google Scholar 

  32. Madnani, N., et al.: Re-examining machine translation metrics for paraphrase identification. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012), PA, USA, pp. 182–190 (2012)

    Google Scholar 

  33. Madnani, N., et al.: Using paraphrases for parameter tuning in statistical machine translation. In: Proceedings of the Second Workshop on Statistical Machine Translation (WMT 2007), Prague, Czech Republic (2007)

    Google Scholar 

  34. Malakasiotis, P.: Paraphrase recognition using machine learning to combine similarity measures. In: Proceedings of the ACL-IJCNLP 2009 Student Research Workshop, Suntec, Singapore, pp. 27–35 (2009)

    Google Scholar 

  35. Marton, Y., et al.: Filtering antonymous, trend-contrasting, and polarity-dissimilar distributional paraphrases for improving statistical machine translation. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 237–249. Association for Computational Linguistics, Edingburgh, Scotland (2011)

    Google Scholar 

  36. Marton, Y., et al.: Improved statistical machine translation using monolingually-derived paraphrases. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore (2009)

    Google Scholar 

  37. Mckeown, K.R.: Paraphrasing questions using given and new information. Comput. Linguist. 9(1), 1–10 (1983)

    MathSciNet  Google Scholar 

  38. Mihalcea, R., et al.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, pp. 775–780. AAAI Press (2006)

    Google Scholar 

  39. Owczarzak, K., et al.: Contextual bitext-derived paraphrases in automatic MT evaluation. In: StatMT 2006, Stroudsburg, PA, USA, pp. 86–93 (2006)

    Google Scholar 

  40. Pedersen, T., Bruce, R.: Knowledge lean word-sense disambiguation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 800–805. AAAI Press (1998)

    Google Scholar 

  41. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al.: Scikit-learn: Machine Learning in Python. http://scikit-learn.org/stable/

  42. Pivovarova, L., et al.: ParaPhraser: Russian paraphrase corpus and shared task. In: Filchenkov, A., et al. (eds.) AINL 2017, CCIS, vol. 789, pp. 211–225. Springer, Cham (2018)

    Google Scholar 

  43. Power, R., Scott, D.: Automatic generation of large-scale paraphrases. In: Proceedings of the 3rd International Workshop on Paraphrasing (IWP2005), Jeju, Republic of Korea, pp. 33–40 (2005)

    Google Scholar 

  44. Pronoza, E., Yagunova, E.: Comparison of sentence similarity measures for Russian paraphrase identification. In: Proceedings of the AINL-ISMW FRUCT 2015, pp. 74–82. IEEE (2015)

    Google Scholar 

  45. Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. (2002)

    Google Scholar 

  46. Rus, V., et al.: On paraphrase identification corpora. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014). European Language Resources Association (ELRA), Reykjavik, Iceland (2014)

    Google Scholar 

  47. Shinyama, Y., et al.: Automatic paraphrase acquisition from news articles. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 313–318 (2002)

    Google Scholar 

  48. Shinyama, Y., Sekine, S.: Paraphrase acquisition for information extraction. In: Proceedings of the second International Workshop on Paraphrasing - Volume 16 (PARAPHRASE 2003), vol. 16, pp. 65–71. Association for Computational Linguistics, Stroudsburg (2003)

    Google Scholar 

  49. Socher, R., et al.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)

    Google Scholar 

  50. Wan, S., et al.: Using dependency-based features to take the ‘Para-farce’ out of paraphrase. In: Proceedings of the Australasian Language Technology Workshop, Sydney, Australia, pp. 131–138 (2006)

    Google Scholar 

  51. Xu, W.: Data-driven approaches for paraphrasing across language variations. New York University (2014)

    Google Scholar 

  52. Zhang, Y., Patrick, J.: Paraphrase identification by text canonicalization. In: proceedings of the Australasian Language Technology Workshop, Sydney, Australia, pp. 160–166 (2005)

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the comments of our reviewers on earlier drafts of this paper.

Author information

Authors and Affiliations

  1. Bartin University, Bartin, 74100, Turkey

    Asli Eyecioglu

  2. University of Sussex, Brighton, BN19QJ, UK

    Bill Keller

Authors
  1. Asli Eyecioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bill Keller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asli Eyecioglu .

Editor information

Editors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  2. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

  3. Mendel University , Brno, Czech Republic

    Jan Žižka

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Eyecioglu, A., Keller, B. (2018). Knowledge-lean Paraphrase Identification Using Character-Based Features. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-71746-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71746-3_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71745-6

  • Online ISBN: 978-3-319-71746-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation